all-text.txt

BERT: Pre-training of Deep Bidirectional Transformers for
Language Understanding
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Kristina Toutanova
Google AI Language
{jacobdevlin,mingweichang,kentonl,kristout}@google.com
Abstract
We introduce a new language representa-
tion model called BERT, which stands for
Bidirectional Encoder Representations from
Transformers. Unlike recent language repre-
sentation models (Peters et al., 2018a; Rad-
ford et al., 2018), BERT is designed to pre-
train deep bidirectional representations from
unlabeled text by jointly conditioning on both
left and right context in all layers. As a re-
sult, the pre-trained BERT model can be fine-
tuned with just one additional output layer
to create state-of-the-art models for a wide
range of tasks, such as question answering and
language inference, without substantial task-
specific architecture modifications.
BERT is conceptually simple and empirically
powerful.
It obtains new state-of-the-art re-
sults on eleven natural language processing
tasks, including pushing the GLUE score to
80.5% (7.7% point absolute improvement),
MultiNLI accuracy to 86.7% (4.6% absolute
improvement), SQuAD v1.1 question answer-
ing Test F1 to 93.2 (1.5 point absolute im-
provement) and SQuAD v2.0 Test F1 to 83.1
(5.1 point absolute improvement).
1
Introduction
Language model pre-training has been shown to
be effective for improving many natural language
processing tasks (Dai and Le, 2015; Peters et al.,
2018a; Radford et al., 2018; Howard and Ruder,
2018). These include sentence-level tasks such as
natural language inference (Bowman et al., 2015;
Williams et al., 2018) and paraphrasing (Dolan
and Brockett, 2005), which aim to predict the re-
lationships between sentences by analyzing them
holistically, as well as token-level tasks such as
named entity recognition and question answering,
where models are required to produce fine-grained
output at the token level (Tjong Kim Sang and
De Meulder, 2003; Rajpurkar et al., 2016).
There are two existing strategies for apply-
ing pre-trained language representations to down-
stream tasks: feature-based and fine-tuning. The
feature-based approach, such as ELMo (Peters
et al., 2018a), uses task-specific architectures that
include the pre-trained representations as addi-
tional features. The fine-tuning approach, such as
the Generative Pre-trained Transformer (OpenAI
GPT) (Radford et al., 2018), introduces minimal
task-specific parameters, and is trained on the
downstream tasks by simply fine-tuning all pre-
trained parameters. The two approaches share the
same objective function during pre-training, where
they use unidirectional language models to learn
general language representations.
We argue that current techniques restrict the
power of the pre-trained representations, espe-
cially for the fine-tuning approaches.
The ma-
jor limitation is that standard language models are
unidirectional, and this limits the choice of archi-
tectures that can be used during pre-training. For
example, in OpenAI GPT, the authors use a left-to-
right architecture, where every token can only at-
tend to previous tokens in the self-attention layers
of the Transformer (Vaswani et al., 2017). Such re-
strictions are sub-optimal for sentence-level tasks,
and could be very harmful when applying fine-
tuning based approaches to token-level tasks such
as question answering, where it is crucial to incor-
porate context from both directions.
In this paper, we improve the fine-tuning based
approaches by proposing BERT: Bidirectional
Encoder
Representations
from
Transformers.
BERT alleviates the previously mentioned unidi-
rectionality constraint by using a “masked lan-
guage model” (MLM) pre-training objective, in-
spired by the Cloze task (Taylor, 1953).
The
masked language model randomly masks some of
the tokens from the input, and the objective is to
predict the original vocabulary id of the masked
arXiv:1810.04805v2  [cs.CL]  24 May 2019
word based only on its context.
Unlike left-to-
right language model pre-training, the MLM ob-
jective enables the representation to fuse the left
and the right context, which allows us to pre-
train a deep bidirectional Transformer. In addi-
tion to the masked language model, we also use
a “next sentence prediction” task that jointly pre-
trains text-pair representations. The contributions
of our paper are as follows:
• We demonstrate the importance of bidirectional
pre-training for language representations. Un-
like Radford et al. (2018), which uses unidirec-
tional language models for pre-training, BERT
uses masked language models to enable pre-
trained deep bidirectional representations. This
is also in contrast to Peters et al. (2018a), which
uses a shallow concatenation of independently
trained left-to-right and right-to-left LMs.
• We show that pre-trained representations reduce
the need for many heavily-engineered task-
specific architectures. BERT is the first fine-
tuning based representation model that achieves
state-of-the-art performance on a large suite
of sentence-level and token-level tasks, outper-
forming many task-specific architectures.
• BERT advances the state of the art for eleven
NLP tasks.
The code and pre-trained mod-
els are available at https://github.com/
google-research/bert.
2
Related Work
There is a long history of pre-training general lan-
guage representations, and we briefly review the
most widely-used approaches in this section.
2.1
Unsupervised Feature-based Approaches
Learning widely applicable representations of
words has been an active area of research for
decades, including non-neural (Brown et al., 1992;
Ando and Zhang, 2005; Blitzer et al., 2006) and
neural (Mikolov et al., 2013; Pennington et al.,
2014) methods.
Pre-trained word embeddings
are an integral part of modern NLP systems, of-
fering significant improvements over embeddings
learned from scratch (Turian et al., 2010). To pre-
train word embedding vectors, left-to-right lan-
guage modeling objectives have been used (Mnih
and Hinton, 2009), as well as objectives to dis-
criminate correct from incorrect words in left and
right context (Mikolov et al., 2013).
These approaches have been generalized to
coarser granularities, such as sentence embed-
dings (Kiros et al., 2015; Logeswaran and Lee,
2018) or paragraph embeddings (Le and Mikolov,
2014).
To train sentence representations, prior
work has used objectives to rank candidate next
sentences (Jernite et al., 2017; Logeswaran and
Lee, 2018), left-to-right generation of next sen-
tence words given a representation of the previous
sentence (Kiros et al., 2015), or denoising auto-
encoder derived objectives (Hill et al., 2016).
ELMo and its predecessor (Peters et al., 2017,
2018a) generalize traditional word embedding re-
search along a different dimension. They extract
context-sensitive features from a left-to-right and a
right-to-left language model. The contextual rep-
resentation of each token is the concatenation of
the left-to-right and right-to-left representations.
When integrating contextual word embeddings
with existing task-specific architectures, ELMo
advances the state of the art for several major NLP
benchmarks (Peters et al., 2018a) including ques-
tion answering (Rajpurkar et al., 2016), sentiment
analysis (Socher et al., 2013), and named entity
recognition (Tjong Kim Sang and De Meulder,
2003). Melamud et al. (2016) proposed learning
contextual representations through a task to pre-
dict a single word from both left and right context
using LSTMs. Similar to ELMo, their model is
feature-based and not deeply bidirectional. Fedus
et al. (2018) shows that the cloze task can be used
to improve the robustness of text generation mod-
els.
2.2
Unsupervised Fine-tuning Approaches
As with the feature-based approaches, the first
works in this direction only pre-trained word em-
bedding parameters from unlabeled text
(Col-
lobert and Weston, 2008).
More recently, sentence or document encoders
which produce contextual token representations
have been pre-trained from unlabeled text and
fine-tuned for a supervised downstream task (Dai
and Le, 2015; Howard and Ruder, 2018; Radford
et al., 2018). The advantage of these approaches
is that few parameters need to be learned from
scratch.
At least partly due to this advantage,
OpenAI GPT (Radford et al., 2018) achieved pre-
viously state-of-the-art results on many sentence-
level tasks from the GLUE benchmark (Wang
et al., 2018a).
Left-to-right language model-
BERT
BERT
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EN
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Tok 1
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Question
Paragraph
Start/End Span
BERT
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EN
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Masked Sentence A
Masked Sentence B
Pre-training
Fine-Tuning
NSP
Mask LM
Mask LM
Unlabeled Sentence A and B Pair
SQuAD
Question Answer Pair
NER
MNLI
Figure 1: Overall pre-training and fine-tuning procedures for BERT. Apart from output layers, the same architec-
tures are used in both pre-training and fine-tuning. The same pre-trained model parameters are used to initialize
models for different down-stream tasks. During fine-tuning, all parameters are fine-tuned. [CLS] is a special
symbol added in front of every input example, and [SEP] is a special separator token (e.g. separating ques-
tions/answers).
ing and auto-encoder objectives have been used
for pre-training such models (Howard and Ruder,
2018; Radford et al., 2018; Dai and Le, 2015).
2.3
Transfer Learning from Supervised Data
There has also been work showing effective trans-
fer from supervised tasks with large datasets, such
as natural language inference (Conneau et al.,
2017) and machine translation (McCann et al.,
2017). Computer vision research has also demon-
strated the importance of transfer learning from
large pre-trained models, where an effective recipe
is to fine-tune models pre-trained with Ima-
geNet (Deng et al., 2009; Yosinski et al., 2014).
3
BERT
We introduce BERT and its detailed implementa-
tion in this section. There are two steps in our
framework: pre-training and fine-tuning.
Dur-
ing pre-training, the model is trained on unlabeled
data over different pre-training tasks.
For fine-
tuning, the BERT model is first initialized with
the pre-trained parameters, and all of the param-
eters are fine-tuned using labeled data from the
downstream tasks. Each downstream task has sep-
arate fine-tuned models, even though they are ini-
tialized with the same pre-trained parameters. The
question-answering example in Figure 1 will serve
as a running example for this section.
A distinctive feature of BERT is its unified ar-
chitecture across different tasks. There is mini-
mal difference between the pre-trained architec-
ture and the final downstream architecture.
Model Architecture
BERT’s model architec-
ture is a multi-layer bidirectional Transformer en-
coder based on the original implementation de-
scribed in Vaswani et al. (2017) and released in
the tensor2tensor library.1 Because the use
of Transformers has become common and our im-
plementation is almost identical to the original,
we will omit an exhaustive background descrip-
tion of the model architecture and refer readers to
Vaswani et al. (2017) as well as excellent guides
such as “The Annotated Transformer.”2
In this work, we denote the number of layers
(i.e., Transformer blocks) as L, the hidden size as
H, and the number of self-attention heads as A.3
We primarily report results on two model sizes:
BERTBASE (L=12, H=768, A=12, Total Param-
eters=110M) and BERTLARGE (L=24, H=1024,
A=16, Total Parameters=340M).
BERTBASE was chosen to have the same model
size as OpenAI GPT for comparison purposes.
Critically, however, the BERT Transformer uses
bidirectional self-attention, while the GPT Trans-
former uses constrained self-attention where every
token can only attend to context to its left.4
1https://github.com/tensorflow/tensor2tensor
2http://nlp.seas.harvard.edu/2018/04/03/attention.html
3In all cases we set the feed-forward/filter size to be 4H,
i.e., 3072 for the H = 768 and 4096 for the H = 1024.
4We note that in the literature the bidirectional Trans-
Input/Output Representations
To make BERT
handle a variety of down-stream tasks, our input
representation is able to unambiguously represent
both a single sentence and a pair of sentences
(e.g., ⟨ Question, Answer ⟩) in one token sequence.
Throughout this work, a “sentence” can be an arbi-
trary span of contiguous text, rather than an actual
linguistic sentence. A “sequence” refers to the in-
put token sequence to BERT, which may be a sin-
gle sentence or two sentences packed together.
We use WordPiece embeddings (Wu et al.,
2016) with a 30,000 token vocabulary. The first
token of every sequence is always a special clas-
sification token ([CLS]). The final hidden state
corresponding to this token is used as the ag-
gregate sequence representation for classification
tasks. Sentence pairs are packed together into a
single sequence. We differentiate the sentences in
two ways. First, we separate them with a special
token ([SEP]). Second, we add a learned embed-
ding to every token indicating whether it belongs
to sentence A or sentence B. As shown in Figure 1,
we denote input embedding as E, the final hidden
vector of the special [CLS] token as C ∈ RH,
and the final hidden vector for the ith input token
as Ti ∈ RH.
For a given token, its input representation is
constructed by summing the corresponding token,
segment, and position embeddings. A visualiza-
tion of this construction can be seen in Figure 2.
3.1
Pre-training BERT
Unlike Peters et al. (2018a) and Radford et al.
(2018), we do not use traditional left-to-right or
right-to-left language models to pre-train BERT.
Instead, we pre-train BERT using two unsuper-
vised tasks, described in this section. This step
is presented in the left part of Figure 1.
Task #1: Masked LM
Intuitively, it is reason-
able to believe that a deep bidirectional model is
strictly more powerful than either a left-to-right
model or the shallow concatenation of a left-to-
right and a right-to-left model.
Unfortunately,
standard conditional language models can only be
trained left-to-right or right-to-left, since bidirec-
tional conditioning would allow each word to in-
directly “see itself”, and the model could trivially
predict the target word in a multi-layered context.
former is often referred to as a “Transformer encoder” while
the left-context-only version is referred to as a “Transformer
decoder” since it can be used for text generation.
In order to train a deep bidirectional representa-
tion, we simply mask some percentage of the input
tokens at random, and then predict those masked
tokens. We refer to this procedure as a “masked
LM” (MLM), although it is often referred to as a
Cloze task in the literature (Taylor, 1953). In this
case, the final hidden vectors corresponding to the
mask tokens are fed into an output softmax over
the vocabulary, as in a standard LM. In all of our
experiments, we mask 15% of all WordPiece to-
kens in each sequence at random. In contrast to
denoising auto-encoders (Vincent et al., 2008), we
only predict the masked words rather than recon-
structing the entire input.
Although this allows us to obtain a bidirec-
tional pre-trained model, a downside is that we
are creating a mismatch between pre-training and
fine-tuning, since the [MASK] token does not ap-
pear during fine-tuning. To mitigate this, we do
not always replace “masked” words with the ac-
tual [MASK] token. The training data generator
chooses 15% of the token positions at random for
prediction. If the i-th token is chosen, we replace
the i-th token with (1) the [MASK] token 80% of
the time (2) a random token 10% of the time (3)
the unchanged i-th token 10% of the time. Then,
Ti will be used to predict the original token with
cross entropy loss. We compare variations of this
procedure in Appendix C.2.
Task #2:
Next Sentence Prediction (NSP)
Many important downstream tasks such as Ques-
tion Answering (QA) and Natural Language Infer-
ence (NLI) are based on understanding the rela-
tionship between two sentences, which is not di-
rectly captured by language modeling. In order
to train a model that understands sentence rela-
tionships, we pre-train for a binarized next sen-
tence prediction task that can be trivially gener-
ated from any monolingual corpus. Specifically,
when choosing the sentences A and B for each pre-
training example, 50% of the time B is the actual
next sentence that follows A (labeled as IsNext),
and 50% of the time it is a random sentence from
the corpus (labeled as NotNext).
As we show
in Figure 1, C is used for next sentence predic-
tion (NSP).5 Despite its simplicity, we demon-
strate in Section 5.1 that pre-training towards this
task is very beneficial to both QA and NLI. 6
5The final model achieves 97%-98% accuracy on NSP.
6The vector C is not a meaningful sentence representation
without fine-tuning, since it was trained with NSP.
[CLS]
he
likes
play
##ing
[SEP]
my
dog
is
cute
[SEP]
Input
E[CLS]
Ehe
Elikes
Eplay
E##ing
E[SEP]
Emy
Edog
Eis
Ecute
E[SEP]
Token
Embeddings
EA
EB
EB
EB
EB
EB
EA
EA
EA
EA
EA
Segment
Embeddings
E0
E6
E7
E8
E9
E10
E1
E2
E3
E4
E5
Position
Embeddings
Figure 2: BERT input representation. The input embeddings are the sum of the token embeddings, the segmenta-
tion embeddings and the position embeddings.
The NSP task is closely related to representation-
learning objectives used in Jernite et al. (2017) and
Logeswaran and Lee (2018). However, in prior
work, only sentence embeddings are transferred to
down-stream tasks, where BERT transfers all pa-
rameters to initialize end-task model parameters.
Pre-training data The pre-training procedure
largely follows the existing literature on language
model pre-training. For the pre-training corpus we
use the BooksCorpus (800M words) (Zhu et al.,
2015) and English Wikipedia (2,500M words).
For Wikipedia we extract only the text passages
and ignore lists, tables, and headers. It is criti-
cal to use a document-level corpus rather than a
shuffled sentence-level corpus such as the Billion
Word Benchmark (Chelba et al., 2013) in order to
extract long contiguous sequences.
3.2
Fine-tuning BERT
Fine-tuning is straightforward since the self-
attention mechanism in the Transformer al-
lows BERT to model many downstream tasks—
whether they involve single text or text pairs—by
swapping out the appropriate inputs and outputs.
For applications involving text pairs, a common
pattern is to independently encode text pairs be-
fore applying bidirectional cross attention, such
as Parikh et al. (2016); Seo et al. (2017). BERT
instead uses the self-attention mechanism to unify
these two stages, as encoding a concatenated text
pair with self-attention effectively includes bidi-
rectional cross attention between two sentences.
For each task, we simply plug in the task-
specific inputs and outputs into BERT and fine-
tune all the parameters end-to-end.
At the in-
put, sentence A and sentence B from pre-training
are analogous to (1) sentence pairs in paraphras-
ing, (2) hypothesis-premise pairs in entailment, (3)
question-passage pairs in question answering, and
(4) a degenerate text-∅ pair in text classification
or sequence tagging. At the output, the token rep-
resentations are fed into an output layer for token-
level tasks, such as sequence tagging or question
answering, and the [CLS] representation is fed
into an output layer for classification, such as en-
tailment or sentiment analysis.
Compared to pre-training, fine-tuning is rela-
tively inexpensive. All of the results in the pa-
per can be replicated in at most 1 hour on a sin-
gle Cloud TPU, or a few hours on a GPU, starting
from the exact same pre-trained model.7 We de-
scribe the task-specific details in the correspond-
ing subsections of Section 4. More details can be
found in Appendix A.5.
4
Experiments
In this section, we present BERT fine-tuning re-
sults on 11 NLP tasks.
4.1
GLUE
The General Language Understanding Evaluation
(GLUE) benchmark (Wang et al., 2018a) is a col-
lection of diverse natural language understanding
tasks. Detailed descriptions of GLUE datasets are
included in Appendix B.1.
To fine-tune on GLUE, we represent the input
sequence (for single sentence or sentence pairs)
as described in Section 3, and use the final hid-
den vector C ∈ RH corresponding to the first
input token ([CLS]) as the aggregate representa-
tion. The only new parameters introduced during
fine-tuning are classification layer weights W ∈
RK×H, where K is the number of labels. We com-
pute a standard classification loss with C and W,
i.e., log(softmax(CW T )).
7For example, the BERT SQuAD model can be trained in
around 30 minutes on a single Cloud TPU to achieve a Dev
F1 score of 91.0%.
8See (10) in https://gluebenchmark.com/faq.
System
MNLI-(m/mm)
QQP
QNLI
SST-2
CoLA
STS-B
MRPC
RTE
Average
392k
363k
108k
67k
8.5k
5.7k
3.5k
2.5k
-
Pre-OpenAI SOTA
80.6/80.1
66.1
82.3
93.2
35.0
81.0
86.0
61.7
74.0
BiLSTM+ELMo+Attn
76.4/76.1
64.8
79.8
90.4
36.0
73.3
84.9
56.8
71.0
OpenAI GPT
82.1/81.4
70.3
87.4
91.3
45.4
80.0
82.3
56.0
75.1
BERTBASE
84.6/83.4
71.2
90.5
93.5
52.1
85.8
88.9
66.4
79.6
BERTLARGE
86.7/85.9
72.1
92.7
94.9
60.5
86.5
89.3
70.1
82.1
Table 1: GLUE Test results, scored by the evaluation server (https://gluebenchmark.com/leaderboard).
The number below each task denotes the number of training examples. The “Average” column is slightly different
than the official GLUE score, since we exclude the problematic WNLI set.8 BERT and OpenAI GPT are single-
model, single task. F1 scores are reported for QQP and MRPC, Spearman correlations are reported for STS-B, and
accuracy scores are reported for the other tasks. We exclude entries that use BERT as one of their components.
We use a batch size of 32 and fine-tune for 3
epochs over the data for all GLUE tasks. For each
task, we selected the best fine-tuning learning rate
(among 5e-5, 4e-5, 3e-5, and 2e-5) on the Dev set.
Additionally, for BERTLARGE we found that fine-
tuning was sometimes unstable on small datasets,
so we ran several random restarts and selected the
best model on the Dev set. With random restarts,
we use the same pre-trained checkpoint but per-
form different fine-tuning data shuffling and clas-
sifier layer initialization.9
Results are presented in Table 1.
Both
BERTBASE and BERTLARGE outperform all sys-
tems on all tasks by a substantial margin, obtaining
4.5% and 7.0% respective average accuracy im-
provement over the prior state of the art. Note that
BERTBASE and OpenAI GPT are nearly identical
in terms of model architecture apart from the at-
tention masking. For the largest and most widely
reported GLUE task, MNLI, BERT obtains a 4.6%
absolute accuracy improvement. On the official
GLUE leaderboard10, BERTLARGE obtains a score
of 80.5, compared to OpenAI GPT, which obtains
72.8 as of the date of writing.
We find that BERTLARGE significantly outper-
forms BERTBASE across all tasks, especially those
with very little training data. The effect of model
size is explored more thoroughly in Section 5.2.
4.2
SQuAD v1.1
The
Stanford
Question
Answering
Dataset
(SQuAD v1.1) is a collection of 100k crowd-
sourced question/answer pairs (Rajpurkar et al.,
2016).
Given a question and a passage from
9The GLUE data set distribution does not include the Test
labels, and we only made a single GLUE evaluation server
submission for each of BERTBASE and BERTLARGE.
10https://gluebenchmark.com/leaderboard
Wikipedia containing the answer, the task is to
predict the answer text span in the passage.
As shown in Figure 1, in the question answer-
ing task, we represent the input question and pas-
sage as a single packed sequence, with the ques-
tion using the A embedding and the passage using
the B embedding. We only introduce a start vec-
tor S ∈ RH and an end vector E ∈ RH during
fine-tuning. The probability of word i being the
start of the answer span is computed as a dot prod-
uct between Ti and S followed by a softmax over
all of the words in the paragraph: Pi =
eS·Ti
�
j eS·Tj .
The analogous formula is used for the end of the
answer span. The score of a candidate span from
position i to position j is defined as S·Ti + E·Tj,
and the maximum scoring span where j ≥ i is
used as a prediction. The training objective is the
sum of the log-likelihoods of the correct start and
end positions. We fine-tune for 3 epochs with a
learning rate of 5e-5 and a batch size of 32.
Table 2 shows top leaderboard entries as well
as results from top published systems (Seo et al.,
2017; Clark and Gardner, 2018; Peters et al.,
2018a; Hu et al., 2018). The top results from the
SQuAD leaderboard do not have up-to-date public
system descriptions available,11 and are allowed to
use any public data when training their systems.
We therefore use modest data augmentation in
our system by first fine-tuning on TriviaQA (Joshi
et al., 2017) befor fine-tuning on SQuAD.
Our best performing system outperforms the top
leaderboard system by +1.5 F1 in ensembling and
+1.3 F1 as a single system. In fact, our single
BERT model outperforms the top ensemble sys-
tem in terms of F1 score. Without TriviaQA fine-
11QANet is described in Yu et al. (2018), but the system
has improved substantially after publication.
System
Dev
Test
EM
F1
EM
F1
Top Leaderboard Systems (Dec 10th, 2018)
Human
-
-
82.3 91.2
#1 Ensemble - nlnet
-
-
86.0 91.7
#2 Ensemble - QANet
-
-
84.5 90.5
Published
BiDAF+ELMo (Single)
-
85.6
-
85.8
R.M. Reader (Ensemble)
81.2 87.9 82.3 88.5
Ours
BERTBASE (Single)
80.8 88.5
-
-
BERTLARGE (Single)
84.1 90.9
-
-
BERTLARGE (Ensemble)
85.8 91.8
-
-
BERTLARGE (Sgl.+TriviaQA)
84.2 91.1 85.1 91.8
BERTLARGE (Ens.+TriviaQA) 86.2 92.2 87.4 93.2
Table 2:
SQuAD 1.1 results. The BERT ensemble
is 7x systems which use different pre-training check-
points and fine-tuning seeds.
System
Dev
Test
EM
F1
EM
F1
Top Leaderboard Systems (Dec 10th, 2018)
Human
86.3 89.0 86.9 89.5
#1 Single - MIR-MRC (F-Net)
-
-
74.8 78.0
#2 Single - nlnet
-
-
74.2 77.1
Published
unet (Ensemble)
-
-
71.4 74.9
SLQA+ (Single)
-
71.4 74.4
Ours
BERTLARGE (Single)
78.7 81.9 80.0 83.1
Table 3: SQuAD 2.0 results. We exclude entries that
use BERT as one of their components.
tuning data, we only lose 0.1-0.4 F1, still outper-
forming all existing systems by a wide margin.12
4.3
SQuAD v2.0
The SQuAD 2.0 task extends the SQuAD 1.1
problem definition by allowing for the possibility
that no short answer exists in the provided para-
graph, making the problem more realistic.
We use a simple approach to extend the SQuAD
v1.1 BERT model for this task. We treat ques-
tions that do not have an answer as having an an-
swer span with start and end at the [CLS] to-
ken. The probability space for the start and end
answer span positions is extended to include the
position of the [CLS] token. For prediction, we
compare the score of the no-answer span: snull =
S·C + E·C to the score of the best non-null span
12The TriviaQA data we used consists of paragraphs from
TriviaQA-Wiki formed of the first 400 tokens in documents,
that contain at least one of the provided possible answers.
System
Dev
Test
ESIM+GloVe
51.9 52.7
ESIM+ELMo
59.1 59.2
OpenAI GPT
-
78.0
BERTBASE
81.6
-
BERTLARGE
86.6 86.3
Human (expert)†
-
85.0
Human (5 annotations)†
-
88.0
Table 4: SWAG Dev and Test accuracies. †Human per-
formance is measured with 100 samples, as reported in
the SWAG paper.
ˆ
si,j = maxj≥iS·Ti + E·Tj. We predict a non-null
answer when ˆ
si,j > snull + τ, where the thresh-
old τ is selected on the dev set to maximize F1.
We did not use TriviaQA data for this model. We
fine-tuned for 2 epochs with a learning rate of 5e-5
and a batch size of 48.
The results compared to prior leaderboard en-
tries and top published work (Sun et al., 2018;
Wang et al., 2018b) are shown in Table 3, exclud-
ing systems that use BERT as one of their com-
ponents. We observe a +5.1 F1 improvement over
the previous best system.
4.4
SWAG
The Situations With Adversarial Generations
(SWAG) dataset contains 113k sentence-pair com-
pletion examples that evaluate grounded common-
sense inference (Zellers et al., 2018). Given a sen-
tence, the task is to choose the most plausible con-
tinuation among four choices.
When fine-tuning on the SWAG dataset, we
construct four input sequences, each containing
the concatenation of the given sentence (sentence
A) and a possible continuation (sentence B). The
only task-specific parameters introduced is a vec-
tor whose dot product with the [CLS] token rep-
resentation C denotes a score for each choice
which is normalized with a softmax layer.
We fine-tune the model for 3 epochs with a
learning rate of 2e-5 and a batch size of 16. Re-
sults are presented in Table 4. BERTLARGE out-
performs the authors’ baseline ESIM+ELMo sys-
tem by +27.1% and OpenAI GPT by 8.3%.
5
Ablation Studies
In this section, we perform ablation experiments
over a number of facets of BERT in order to better
understand their relative importance. Additional
Dev Set
Tasks
MNLI-m QNLI MRPC SST-2 SQuAD
(Acc)
(Acc)
(Acc)
(Acc)
(F1)
BERTBASE
84.4
88.4
86.7
92.7
88.5
No NSP
83.9
84.9
86.5
92.6
87.9
LTR & No NSP
82.1
84.3
77.5
92.1
77.8
+ BiLSTM
82.1
84.1
75.7
91.6
84.9
Table 5: Ablation over the pre-training tasks using the
BERTBASE architecture. “No NSP” is trained without
the next sentence prediction task. “LTR & No NSP” is
trained as a left-to-right LM without the next sentence
prediction, like OpenAI GPT. “+ BiLSTM” adds a ran-
domly initialized BiLSTM on top of the “LTR + No
NSP” model during fine-tuning.
ablation studies can be found in Appendix C.
5.1
Effect of Pre-training Tasks
We demonstrate the importance of the deep bidi-
rectionality of BERT by evaluating two pre-
training objectives using exactly the same pre-
training data, fine-tuning scheme, and hyperpa-
rameters as BERTBASE:
No NSP: A bidirectional model which is trained
using the “masked LM” (MLM) but without the
“next sentence prediction” (NSP) task.
LTR & No NSP: A left-context-only model which
is trained using a standard Left-to-Right (LTR)
LM, rather than an MLM. The left-only constraint
was also applied at fine-tuning, because removing
it introduced a pre-train/fine-tune mismatch that
degraded downstream performance. Additionally,
this model was pre-trained without the NSP task.
This is directly comparable to OpenAI GPT, but
using our larger training dataset, our input repre-
sentation, and our fine-tuning scheme.
We first examine the impact brought by the NSP
task.
In Table 5, we show that removing NSP
hurts performance significantly on QNLI, MNLI,
and SQuAD 1.1. Next, we evaluate the impact
of training bidirectional representations by com-
paring “No NSP” to “LTR & No NSP”. The LTR
model performs worse than the MLM model on all
tasks, with large drops on MRPC and SQuAD.
For SQuAD it is intuitively clear that a LTR
model will perform poorly at token predictions,
since the token-level hidden states have no right-
side context. In order to make a good faith at-
tempt at strengthening the LTR system, we added
a randomly initialized BiLSTM on top. This does
significantly improve results on SQuAD, but the
results are still far worse than those of the pre-
trained bidirectional models. The BiLSTM hurts
performance on the GLUE tasks.
We recognize that it would also be possible to
train separate LTR and RTL models and represent
each token as the concatenation of the two mod-
els, as ELMo does. However: (a) this is twice as
expensive as a single bidirectional model; (b) this
is non-intuitive for tasks like QA, since the RTL
model would not be able to condition the answer
on the question; (c) this it is strictly less powerful
than a deep bidirectional model, since it can use
both left and right context at every layer.
5.2
Effect of Model Size
In this section, we explore the effect of model size
on fine-tuning task accuracy. We trained a number
of BERT models with a differing number of layers,
hidden units, and attention heads, while otherwise
using the same hyperparameters and training pro-
cedure as described previously.
Results on selected GLUE tasks are shown in
Table 6. In this table, we report the average Dev
Set accuracy from 5 random restarts of fine-tuning.
We can see that larger models lead to a strict ac-
curacy improvement across all four datasets, even
for MRPC which only has 3,600 labeled train-
ing examples, and is substantially different from
the pre-training tasks. It is also perhaps surpris-
ing that we are able to achieve such significant
improvements on top of models which are al-
ready quite large relative to the existing literature.
For example, the largest Transformer explored in
Vaswani et al. (2017) is (L=6, H=1024, A=16)
with 100M parameters for the encoder, and the
largest Transformer we have found in the literature
is (L=64, H=512, A=2) with 235M parameters
(Al-Rfou et al., 2018). By contrast, BERTBASE
contains 110M parameters and BERTLARGE con-
tains 340M parameters.
It has long been known that increasing the
model size will lead to continual improvements
on large-scale tasks such as machine translation
and language modeling, which is demonstrated
by the LM perplexity of held-out training data
shown in Table 6.
However, we believe that
this is the first work to demonstrate convinc-
ingly that scaling to extreme model sizes also
leads to large improvements on very small scale
tasks, provided that the model has been suffi-
ciently pre-trained. Peters et al. (2018b) presented
mixed results on the downstream task impact of
increasing the pre-trained bi-LM size from two
to four layers and Melamud et al. (2016) men-
tioned in passing that increasing hidden dimen-
sion size from 200 to 600 helped, but increasing
further to 1,000 did not bring further improve-
ments. Both of these prior works used a feature-
based approach — we hypothesize that when the
model is fine-tuned directly on the downstream
tasks and uses only a very small number of ran-
domly initialized additional parameters, the task-
specific models can benefit from the larger, more
expressive pre-trained representations even when
downstream task data is very small.
5.3
Feature-based Approach with BERT
All of the BERT results presented so far have used
the fine-tuning approach, where a simple classifi-
cation layer is added to the pre-trained model, and
all parameters are jointly fine-tuned on a down-
stream task. However, the feature-based approach,
where fixed features are extracted from the pre-
trained model, has certain advantages. First, not
all tasks can be easily represented by a Trans-
former encoder architecture, and therefore require
a task-specific model architecture to be added.
Second, there are major computational benefits
to pre-compute an expensive representation of the
training data once and then run many experiments
with cheaper models on top of this representation.
In this section, we compare the two approaches
by applying BERT to the CoNLL-2003 Named
Entity Recognition (NER) task (Tjong Kim Sang
and De Meulder, 2003). In the input to BERT, we
use a case-preserving WordPiece model, and we
include the maximal document context provided
by the data. Following standard practice, we for-
mulate this as a tagging task but do not use a CRF
Hyperparams
Dev Set Accuracy
#L
#H #A LM (ppl) MNLI-m MRPC SST-2
3
768
12
5.84
77.9
79.8
88.4
6
768
3
5.24
80.6
82.2
90.7
6
768
12
4.68
81.9
84.8
91.3
12
768
12
3.99
84.4
86.7
92.9
12 1024
16
3.54
85.7
86.9
93.3
24 1024
16
3.23
86.6
87.8
93.7
Table 6:
Ablation over BERT model size. #L = the
number of layers; #H = hidden size; #A = number of at-
tention heads. “LM (ppl)” is the masked LM perplexity
of held-out training data.
System
Dev F1 Test F1
ELMo (Peters et al., 2018a)
95.7
92.2
CVT (Clark et al., 2018)
-
92.6
CSE (Akbik et al., 2018)
-
93.1
Fine-tuning approach
BERTLARGE
96.6
92.8
BERTBASE
96.4
92.4
Feature-based approach (BERTBASE)
Embeddings
91.0
-
Second-to-Last Hidden
95.6
-
Last Hidden
94.9
-
Weighted Sum Last Four Hidden
95.9
-
Concat Last Four Hidden
96.1
-
Weighted Sum All 12 Layers
95.5
-
Table 7: CoNLL-2003 Named Entity Recognition re-
sults. Hyperparameters were selected using the Dev
set. The reported Dev and Test scores are averaged over
5 random restarts using those hyperparameters.
layer in the output. We use the representation of
the first sub-token as the input to the token-level
classifier over the NER label set.
To ablate the fine-tuning approach, we apply the
feature-based approach by extracting the activa-
tions from one or more layers without fine-tuning
any parameters of BERT. These contextual em-
beddings are used as input to a randomly initial-
ized two-layer 768-dimensional BiLSTM before
the classification layer.
Results are presented in Table 7. BERTLARGE
performs competitively with state-of-the-art meth-
ods. The best performing method concatenates the
token representations from the top four hidden lay-
ers of the pre-trained Transformer, which is only
0.3 F1 behind fine-tuning the entire model. This
demonstrates that BERT is effective for both fine-
tuning and feature-based approaches.
6
Conclusion
Recent empirical improvements due to transfer
learning with language models have demonstrated
that rich, unsupervised pre-training is an integral
part of many language understanding systems. In
particular, these results enable even low-resource
tasks to benefit from deep unidirectional architec-
tures. Our major contribution is further general-
izing these findings to deep bidirectional architec-
tures, allowing the same pre-trained model to suc-
cessfully tackle a broad set of NLP tasks.
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Appendix for “BERT: Pre-training of
Deep Bidirectional Transformers for
Language Understanding”
We organize the appendix into three sections:
• Additional implementation details for BERT
are presented in Appendix A;
• Additional details for our experiments are
presented in Appendix B; and
• Additional ablation studies are presented in
Appendix C.
We present additional ablation studies for
BERT including:
– Effect of Number of Training Steps; and
– Ablation for Different Masking Proce-
dures.
A
Additional Details for BERT
A.1
Illustration of the Pre-training Tasks
We provide examples of the pre-training tasks in
the following.
Masked LM and the Masking Procedure
As-
suming the unlabeled sentence is
my dog is
hairy, and during the random masking procedure
we chose the 4-th token (which corresponding to
hairy), our masking procedure can be further il-
lustrated by
• 80% of the time: Replace the word with the
[MASK] token, e.g., my dog is hairy →
my dog is [MASK]
• 10% of the time: Replace the word with a
random word, e.g., my dog is hairy → my
dog is apple
• 10% of the time:
Keep the word un-
changed, e.g., my dog is hairy → my dog
is hairy. The purpose of this is to bias the
representation towards the actual observed
word.
The advantage of this procedure is that the
Transformer encoder does not know which words
it will be asked to predict or which have been re-
placed by random words, so it is forced to keep
a distributional contextual representation of ev-
ery input token.
Additionally, because random
replacement only occurs for 1.5% of all tokens
(i.e., 10% of 15%), this does not seem to harm
the model’s language understanding capability. In
Section C.2, we evaluate the impact this proce-
dure.
Compared to standard langauge model training,
the masked LM only make predictions on 15% of
tokens in each batch, which suggests that more
pre-training steps may be required for the model
BERT (Ours)
Trm
Trm
Trm
Trm
Trm
Trm
...
...
Trm
Trm
Trm
Trm
Trm
Trm
...
...
OpenAI GPT
Lstm
ELMo
Lstm
Lstm
Lstm
Lstm
Lstm
Lstm
Lstm
Lstm
Lstm
Lstm
Lstm
T1
T2
TN
...
...
...
...
...
E1
E2
EN
...
T1
T2
TN
...
E1
E2
EN
...
T1
T2
TN
...
E1
E2
EN
...
Figure 3: Differences in pre-training model architectures. BERT uses a bidirectional Transformer. OpenAI GPT
uses a left-to-right Transformer. ELMo uses the concatenation of independently trained left-to-right and right-to-
left LSTMs to generate features for downstream tasks. Among the three, only BERT representations are jointly
conditioned on both left and right context in all layers. In addition to the architecture differences, BERT and
OpenAI GPT are fine-tuning approaches, while ELMo is a feature-based approach.
to converge. In Section C.1 we demonstrate that
MLM does converge marginally slower than a left-
to-right model (which predicts every token), but
the empirical improvements of the MLM model
far outweigh the increased training cost.
Next Sentence Prediction
The next sentence
prediction task can be illustrated in the following
examples.
Input = [CLS] the man went to [MASK] store [SEP]
he bought a gallon [MASK] milk [SEP]
Label = IsNext
Input = [CLS] the man [MASK] to the store [SEP]
penguin [MASK] are flight ##less birds [SEP]
Label = NotNext
A.2
Pre-training Procedure
To generate each training input sequence, we sam-
ple two spans of text from the corpus, which we
refer to as “sentences” even though they are typ-
ically much longer than single sentences (but can
be shorter also). The first sentence receives the A
embedding and the second receives the B embed-
ding. 50% of the time B is the actual next sentence
that follows A and 50% of the time it is a random
sentence, which is done for the “next sentence pre-
diction” task. They are sampled such that the com-
bined length is ≤ 512 tokens. The LM masking is
applied after WordPiece tokenization with a uni-
form masking rate of 15%, and no special consid-
eration given to partial word pieces.
We train with batch size of 256 sequences (256
sequences * 512 tokens = 128,000 tokens/batch)
for 1,000,000 steps, which is approximately 40
epochs over the 3.3 billion word corpus.
We
use Adam with learning rate of 1e-4, β1 = 0.9,
β2 = 0.999, L2 weight decay of 0.01, learning
rate warmup over the first 10,000 steps, and linear
decay of the learning rate. We use a dropout prob-
ability of 0.1 on all layers. We use a gelu acti-
vation (Hendrycks and Gimpel, 2016) rather than
the standard relu, following OpenAI GPT. The
training loss is the sum of the mean masked LM
likelihood and the mean next sentence prediction
likelihood.
Training of BERTBASE was performed on 4
Cloud TPUs in Pod configuration (16 TPU chips
total).13 Training of BERTLARGE was performed
on 16 Cloud TPUs (64 TPU chips total). Each pre-
training took 4 days to complete.
Longer sequences are disproportionately expen-
sive because attention is quadratic to the sequence
length. To speed up pretraing in our experiments,
we pre-train the model with sequence length of
128 for 90% of the steps. Then, we train the rest
10% of the steps of sequence of 512 to learn the
positional embeddings.
A.3
Fine-tuning Procedure
For fine-tuning, most model hyperparameters are
the same as in pre-training, with the exception of
the batch size, learning rate, and number of train-
ing epochs. The dropout probability was always
kept at 0.1. The optimal hyperparameter values
are task-specific, but we found the following range
of possible values to work well across all tasks:
• Batch size: 16, 32
13https://cloudplatform.googleblog.com/2018/06/Cloud-
TPU-now-offers-preemptible-pricing-and-global-
availability.html
• Learning rate (Adam): 5e-5, 3e-5, 2e-5
• Number of epochs: 2, 3, 4
We also observed that large data sets (e.g.,
100k+ labeled training examples) were far less
sensitive to hyperparameter choice than small data
sets. Fine-tuning is typically very fast, so it is rea-
sonable to simply run an exhaustive search over
the above parameters and choose the model that
performs best on the development set.
A.4
Comparison of BERT, ELMo ,and
OpenAI GPT
Here we studies the differences in recent popular
representation learning models including ELMo,
OpenAI GPT and BERT. The comparisons be-
tween the model architectures are shown visually
in Figure 3. Note that in addition to the architec-
ture differences, BERT and OpenAI GPT are fine-
tuning approaches, while ELMo is a feature-based
approach.
The most comparable existing pre-training
method to BERT is OpenAI GPT, which trains a
left-to-right Transformer LM on a large text cor-
pus. In fact, many of the design decisions in BERT
were intentionally made to make it as close to
GPT as possible so that the two methods could be
minimally compared. The core argument of this
work is that the bi-directionality and the two pre-
training tasks presented in Section 3.1 account for
the majority of the empirical improvements, but
we do note that there are several other differences
between how BERT and GPT were trained:
• GPT is trained on the BooksCorpus (800M
words); BERT is trained on the BooksCor-
pus (800M words) and Wikipedia (2,500M
words).
• GPT uses a sentence separator ([SEP]) and
classifier token ([CLS]) which are only in-
troduced at fine-tuning time; BERT learns
[SEP], [CLS] and sentence A/B embed-
dings during pre-training.
• GPT was trained for 1M steps with a batch
size of 32,000 words; BERT was trained for
1M steps with a batch size of 128,000 words.
• GPT used the same learning rate of 5e-5 for
all fine-tuning experiments; BERT chooses a
task-specific fine-tuning learning rate which
performs the best on the development set.
To isolate the effect of these differences, we per-
form ablation experiments in Section 5.1 which
demonstrate that the majority of the improvements
are in fact coming from the two pre-training tasks
and the bidirectionality they enable.
A.5
Illustrations of Fine-tuning on Different
Tasks
The illustration of fine-tuning BERT on different
tasks can be seen in Figure 4. Our task-specific
models are formed by incorporating BERT with
one additional output layer, so a minimal num-
ber of parameters need to be learned from scratch.
Among the tasks, (a) and (b) are sequence-level
tasks while (c) and (d) are token-level tasks. In
the figure, E represents the input embedding, Ti
represents the contextual representation of token i,
[CLS] is the special symbol for classification out-
put, and [SEP] is the special symbol to separate
non-consecutive token sequences.
B
Detailed Experimental Setup
B.1
Detailed Descriptions for the GLUE
Benchmark Experiments.
Our
GLUE
results
in
Table1
are
obtained
from
https://gluebenchmark.com/
leaderboard
and
https://blog.
openai.com/language-unsupervised.
The GLUE benchmark includes the following
datasets, the descriptions of which were originally
summarized in Wang et al. (2018a):
MNLI
Multi-Genre Natural Language Inference
is a large-scale, crowdsourced entailment classifi-
cation task (Williams et al., 2018). Given a pair of
sentences, the goal is to predict whether the sec-
ond sentence is an entailment, contradiction, or
neutral with respect to the first one.
QQP
Quora Question Pairs is a binary classifi-
cation task where the goal is to determine if two
questions asked on Quora are semantically equiv-
alent (Chen et al., 2018).
QNLI
Question Natural Language Inference is
a version of the Stanford Question Answering
Dataset (Rajpurkar et al., 2016) which has been
converted to a binary classification task (Wang
et al., 2018a). The positive examples are (ques-
tion, sentence) pairs which do contain the correct
answer, and the negative examples are (question,
sentence) from the same paragraph which do not
contain the answer.
BERT
E[CLS]
E1
E[SEP]
...
EN
E1’
...
EM’
C
T1
T[SEP]
...
TN
T1’
...
TM’
[CLS]
Tok
1
[SEP]
...
Tok
N
Tok
1
...
Tok
M
Question
Paragraph
BERT
E[CLS]
E1
E2
EN
C
T1
T2
TN
Single Sentence
...
...
BERT
Tok 1
Tok 2
Tok N
...
[CLS]
E[CLS]
E1
E2
EN
C
T1
T2
TN
Single Sentence
B-PER
O
O
...
...
E[CLS]
E1
E[SEP]
Class
Label
...
EN
E1’
...
EM’
C
T1
T[SEP]
...
TN
T1’
...
TM’
Start/End Span
Class
Label
BERT
Tok 1
Tok 2
Tok N
...
[CLS]
Tok 1
[CLS]
[CLS]
Tok
1
[SEP]
...
Tok
N
Tok
1
...
Tok
M
Sentence 1
...
Sentence 2
Figure 4: Illustrations of Fine-tuning BERT on Different Tasks.
SST-2
The Stanford Sentiment Treebank is a
binary single-sentence classification task consist-
ing of sentences extracted from movie reviews
with human annotations of their sentiment (Socher
et al., 2013).
CoLA
The Corpus of Linguistic Acceptability is
a binary single-sentence classification task, where
the goal is to predict whether an English sentence
is linguistically “acceptable” or not (Warstadt
et al., 2018).
STS-B
The Semantic Textual Similarity Bench-
mark is a collection of sentence pairs drawn from
news headlines and other sources (Cer et al.,
2017). They were annotated with a score from 1
to 5 denoting how similar the two sentences are in
terms of semantic meaning.
MRPC
Microsoft Research Paraphrase Corpus
consists of sentence pairs automatically extracted
from online news sources, with human annotations
for whether the sentences in the pair are semanti-
cally equivalent (Dolan and Brockett, 2005).
RTE
Recognizing Textual Entailment is a bi-
nary entailment task similar to MNLI, but with
much less training data (Bentivogli et al., 2009).14
WNLI
Winograd NLI is a small natural lan-
guage inference dataset (Levesque et al., 2011).
The GLUE webpage notes that there are issues
with the construction of this dataset, 15 and every
trained system that’s been submitted to GLUE has
performed worse than the 65.1 baseline accuracy
of predicting the majority class. We therefore ex-
clude this set to be fair to OpenAI GPT. For our
GLUE submission, we always predicted the ma-
14Note that we only report single-task fine-tuning results
in this paper. A multitask fine-tuning approach could poten-
tially push the performance even further. For example, we
did observe substantial improvements on RTE from multi-
task training with MNLI.
15https://gluebenchmark.com/faq
jority class.
C
Additional Ablation Studies
C.1
Effect of Number of Training Steps
Figure 5 presents MNLI Dev accuracy after fine-
tuning from a checkpoint that has been pre-trained
for k steps. This allows us to answer the following
questions:
1. Question:
Does BERT really need such
a large amount of pre-training (128,000
words/batch * 1,000,000 steps) to achieve
high fine-tuning accuracy?
Answer: Yes, BERTBASE achieves almost
1.0% additional accuracy on MNLI when
trained on 1M steps compared to 500k steps.
2. Question: Does MLM pre-training converge
slower than LTR pre-training, since only 15%
of words are predicted in each batch rather
than every word?
Answer: The MLM model does converge
slightly slower than the LTR model. How-
ever, in terms of absolute accuracy the MLM
model begins to outperform the LTR model
almost immediately.
C.2
Ablation for Different Masking
Procedures
In Section 3.1, we mention that BERT uses a
mixed strategy for masking the target tokens when
pre-training with the masked language model
(MLM) objective. The following is an ablation
study to evaluate the effect of different masking
strategies.
200
400
600
800
1,000
76
78
80
82
84
Pre-training Steps (Thousands)
MNLI Dev Accuracy
BERTBASE (Masked LM)
BERTBASE (Left-to-Right)
Figure 5: Ablation over number of training steps. This
shows the MNLI accuracy after fine-tuning, starting
from model parameters that have been pre-trained for
k steps. The x-axis is the value of k.
Note that the purpose of the masking strategies
is to reduce the mismatch between pre-training
and fine-tuning, as the [MASK] symbol never ap-
pears during the fine-tuning stage. We report the
Dev results for both MNLI and NER. For NER,
we report both fine-tuning and feature-based ap-
proaches, as we expect the mismatch will be am-
plified for the feature-based approach as the model
will not have the chance to adjust the representa-
tions.
Masking Rates
Dev Set Results
MASK SAME
RND
MNLI
NER
Fine-tune Fine-tune Feature-based
80%
10%
10%
84.2
95.4
94.9
100%
0%
0%
84.3
94.9
94.0
80%
0%
20%
84.1
95.2
94.6
80%
20%
0%
84.4
95.2
94.7
0%
20%
80%
83.7
94.8
94.6
0%
0% 100%
83.6
94.9
94.6
Table 8: Ablation over different masking strategies.
The results are presented in Table 8. In the table,
MASK means that we replace the target token with
the [MASK] symbol for MLM; SAME means that
we keep the target token as is; RND means that
we replace the target token with another random
token.
The numbers in the left part of the table repre-
sent the probabilities of the specific strategies used
during MLM pre-training (BERT uses 80%, 10%,
10%). The right part of the paper represents the
Dev set results. For the feature-based approach,
we concatenate the last 4 layers of BERT as the
features, which was shown to be the best approach
in Section 5.3.
From the table it can be seen that fine-tuning is
surprisingly robust to different masking strategies.
However, as expected, using only the MASK strat-
egy was problematic when applying the feature-
based approach to NER. Interestingly, using only
the RND strategy performs much worse than our
strategy as well.