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slidesoracle.tex
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\documentclass{beamer}
% \mode<presentation>
\setbeamertemplate{navigation symbols}{}
\let\tempone\itemize
\let\temptwo\enditemize
\renewenvironment{itemize}{\tempone\addtolength{\itemsep}{0.5\baselineskip}}{\temptwo}
\usepackage{beamerthemeshadow}
\usepackage{ulem}
\usepackage{tikz}
\usepackage{hyperref}
\usepackage{natbib}
\usepackage{pgffor}
\usepackage{booktabs}
\usepackage{graphicx}
\usepackage{amssymb}
\usepackage{tabularx}
\usepackage{tikz,etoolbox}
\usepackage{tikz,amsmath,siunitx}
\usetikzlibrary{arrows,snakes,backgrounds,patterns,matrix,shapes,fit,calc,shadows,plotmarks}
\usepackage{subcaption}
% \usepackage{url}
% \usepackage{hyperref}
\usepackage{pgf}
\usepackage{latexsym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsthm}
\usepackage{algorithm}
\usepackage{amsmath}
\usepackage{tabularx}
\usepackage{xcolor}
\usepackage[absolute,overlay]{textpos}
\usetikzlibrary{shapes,arrows,positioning,automata,positioning,spy,matrix,scopes,chains}
\newcommand{\digs}[2]{\hphantom{999}\llap{#1}\,+\,\hphantom{999}\llap{#2}}
\setbeamersize{text margin left=6mm}
\setbeamersize{text margin right=6mm}
\renewcommand{\insertnavigation}[1]{}
\setbeamertemplate{headline}{}
\setbeamertemplate{footline}{}
\usefonttheme{professionalfonts}
% make itemize things larger
%\setbeamerfont*{itemize/enumerate body}{size=\Large}
%\setbeamerfont*{itemize/enumerate subbody}{size=\large}
\setbeamercovered{transparent}
\mode<presentation>
%\mode<handout>
\linespread{1.25}
\DeclareMathOperator{\Tr}{Tr}
\usepackage{color}
\usepackage{multirow}
\usepackage{rotating}
\usepackage[all,dvips]{xy}
\usepackage{colortbl}
\usepackage{graphicx}
\usepackage{verbatim}
\usepackage{framed}
\usepackage{natbib}
\usepackage[labelformat=empty]{caption}
\newcommand{\air}{\vspace{0.25cm}}
\newcommand{\mair}{\vspace{-0.25cm}}
\setbeamertemplate{navigation symbols}{}%remove navigation symbols
\renewcommand{\rmdefault}{crm}
\newcommand{\lnbrack}{{\normalfont [}}
\newcommand{\rnbrack}{{\normalfont ]}\thinspace}
\newcommand{\lbbrack}{\textcolor{red}{\textbf{[}}}
\newcommand{\rbbrack}{\textcolor{red}{\textbf{]}}\thinspace}
\definecolor{vermillion}{RGB}{213,94,0}
\newcommand{\given}{\,|\,}
\definecolor{orange}{RGB}{230,159,0}
\definecolor{skyblue}{RGB}{86,180,233}
\definecolor{bluegreen}{RGB}{90,143,41}
% \definecolor{bluegreen}{RGB}{0,158,115}
\definecolor{myyellow}{RGB}{240,228,66} % i dunno if this is the same as standard yellow
\definecolor{myblue}{RGB}{0,114,178}
\definecolor{vermillion}{RGB}{213,94,0}
\definecolor{redpurple}{RGB}{204,121,167}
\definecolor{lightgrey}{RGB}{234,234,234}
\newcommand{\ha}{\boldh_{\ua}}
\newcommand{\hp}{\boldh_{\up}}
\newcommand{\hc}{\boldh_{\mathrm{c}}}
\usetikzlibrary{positioning}
% \setbeamerfont{alerted text}{series=\bfseries}
% \setbeamerfont{structure}{series=\bfseries}
% Needed for diakgrams.
\def\im#1#2{
\node(#1) [scale=#2]{\pgfbox[center,top]{\pgfuseimage{#1}}
};}
% \input{pictures_header}
\title[Seq2seq]{Interpreting, Training, and Distilling Seq2Seq Models}
\author[Alexander Rush]{Alexander Rush (@harvardnlp) \\
{\scriptsize (with Yoon Kim, Sam Wiseman, Allen Schmaltz, Sebastian Gehrmann, Hendrik Strobelt) } \\}
\institute[Harvard SEAS]{ \\
\begin{center}
\vspace{-0.5cm}
\includegraphics[width=1.7cm]{seas}
\end{center}
at \\
\begin{center}
\includegraphics[width=2cm]{oracle}
\end{center}
}
\date{}
% \usetheme{Madrid}
\newcommand{\enc}{\mathrm{src}}
\newcommand{\xvec}{\mathbf{x}}
\newcommand{\yvec}{\mathbf{y}}
\newcommand{\wvec}{\mathbf{w}}
\newcommand{\cvec}{\mathbf{c}}
\newcommand{\zvec}{\mathbf{z}}
% \newcommand{\mcY}{\mathcal{Y}}
% \newcommand{\mcV}{\mathcal{V}}
\newcommand{\context}{\mathbf{w}_{\mathrm{c}}}
\newcommand{\embcontext}{\mathbf{\tilde{w}}_{\mathrm{c}}}
\newcommand{\inpcontext}{\mathbf{\tilde{x}}}
\newcommand{\start}{\mathbf{\tilde{y}}_{\mathrm{c0}}}
\newcommand{\End}{\mathrm{\texttt{</s>}}}
\newcommand{\Uvec}{\mathbf{U}}
\newcommand{\Evec}{\mathbf{E}}
\newcommand{\Gvec}{\mathbf{G}}
\newcommand{\Fvec}{\mathbf{F}}
\newcommand{\Pvec}{\mathbf{P}}
\newcommand{\pvec}{\mathbf{p}}
\newcommand{\Qvec}{\mathbf{Q}}
\newcommand{\Vvec}{\mathbf{V}}
\newcommand{\Wvec}{\mathbf{W}}
\newcommand{\hvec}{\mathbf{h}}
% \newcommand{\reals}{\mathbb{R}}
\newcommand{\Cite}[1]{{\footnotesize \citep{#1}}}
\newcommand{\TT}[1]{{\footnotesize\tt{#1}}}
\newcommand{\boldw}{\boldsymbol{w}}
\newcommand{\boldu}{\boldsymbol{u}}
\newcommand{\boldv}{\boldsymbol{v}}
\newcommand{\boldb}{\boldsymbol{b}}
\newcommand{\boldW}{\boldsymbol{W}}
\newcommand{\boldh}{\boldsymbol{h}}
\newcommand{\boldg}{\boldsymbol{g}}
\newcommand{\ua}{\ensuremath{\mathrm{a}}}
\newcommand{\up}{\ensuremath{\mathrm{p}}}
%\newcommand{\bphi}{\ensuremath{\mathbf{\phi}}}
\newcommand{\bphi}{\boldsymbol{\phi}}
\newcommand{\btheta}{\boldsymbol{\theta}}
\newcommand{\mcY}{\mathcal{Y}}
\newcommand{\mcX}{\mathcal{X}}
\newcommand{\mcC}{\mathcal{C}}
\newcommand{\mcA}{\mathcal{A}}
\newcommand{\mcV}{\mathcal{V}}
\newcommand{\trans}{\ensuremath{\mathsf{T}}}
\def\argmin{\operatornamewithlimits{arg\,min}}
\def\argmax{\operatornamewithlimits{arg\,max}}
\newcommand{\reals}{\ensuremath{\mathbb{R}}}
\newcommand{\aphi}{\boldsymbol{\phi}_{\mathrm{a}}}
\newcommand{\pwphi}{\boldsymbol{\phi}_{\mathrm{p}}}
\newcommand{\squigaphi}{\widetilde{\boldsymbol{\phi}}_{\mathrm{a}}}
\newcommand{\squigpwphi}{\widetilde{\boldsymbol{\phi}}_{\mathrm{p}}}
\newcommand{\aW}{\boldW_{\mathrm{\ua}}}
\newcommand{\pW}{\boldW_{\mathrm{\up}}}
\newcommand{\ab}{\boldb_{\mathrm{\ua}}}
\newcommand{\pb}{\boldb_{\mathrm{\up}}}
\newcommand{\Da}{d_{\mathrm{a}}}
\newcommand{\Dp}{d_{\mathrm{p}}}
% \newcommand{\ha}{\boldh_{\ua}}
% \newcommand{\hp}{\boldh_{\up}}
\newcommand{\ourmodel}{This work}
\newcommand{\zro}{{\color{white}0}}
\def\argmax{\operatornamewithlimits{arg\,max}}
\def\kargmax{\operatornamewithlimits{K-arg\,max}}
\begin{document}
\begin{frame}
\titlepage
\end{frame}
\note{
Thank you for inviting me today. It is an honor to
give a talk here at the Center for Language And Speech Technology.}
% % \begin{frame}{Introduction}
% % \begin{columns}
% % \begin{column}{0.7\textwidth}
% % \structure{Previous:}
% % \begin{itemize}
% % \item \structure{Degree:}
% % \begin{itemize}
% % \item MIT with Prof. Michael Collins
% % \item Visiting Scholar at Columbia University
% % \end{itemize}
% % \item \structure{Intern:}
% % \begin{itemize}
% % \item Google NLP Research (NYC)
% % \end{itemize}
% % \item\structure{Post-Doc}
% % \begin{itemize}
% % \item Facebook AI Research Lab (NYC)
% % \item Group run by Yann LeCun
% % \end{itemize}
% % \end{itemize}
% % \structure{Assistant Professor} at Harvard SEAS
% % \begin{itemize}
% % \item CS182: Artificial Intelligence
% % \item CS287: Natural Language Processing
% % \end{itemize}
% % \end{column}
% % \begin{column}{0.3\textwidth}
% % \begin{center}
% % \includegraphics[width=3cm]{pic1}
% % \end{center}
% % \end{column}
% % \end{columns}
% % \end{frame}
% % \begin{frame}{Contents}
% % \tableofcontents
% % \end{frame}
% \section{Deep Learning For Language}
% \begin{frame}% {Deep Learning in NLP}
% % \begin{itemize}
% % \item<1>
% \begin{center}
% \structure{Deep Learning for NLP}
% \end{center}
% \pause
% \begin{block}{}
% \begin{quote}
% Deep Learning waves have lapped at the shores of computational
% linguistics for several years now, but 2015 seems like the year
% when the full force of the tsunami hit major NLP
% conferences. {\normalfont }
% \end{quote}
% \end{block}
% \begin{flushright}
% - Chris Manning (Computational Linguistics and Deep Learning)
% \end{flushright}
% % \air
% % \item<2>
% % \begin{quote}
% % The next big step for Deep Learning is natural language
% % understanding, which aims to give machines the power to understand
% % not just individual words but entire sentence and paragraphs. {\normalfont - Yoshua Bengio }
% % \end{quote}
% % \end{itemize}
% \end{frame}
% \note{
% In the last several years, the field of natural language processing
% has seen a sea-change in the methods and models used for common problems.
% The advent and success of deep learning systems for natural language,
% has caused many people to rethink basic assumptions in the field.
% Stanford Professor Chris manning published his thoughts on the
% matter in a essay earlier this year, based on his keynote at ACL.
% And I will borrow several of his thoughts throughout this talk.
% He begins by observing that
% Deep Learning waves have lapped at the shores of computational
% linguistics for several years now, but 2015 seems like the year
% when the full force of the tsunami hit major NLP
% conferences.
% Now, unfortunately the term deep learning is ill-defined.
% But we can give a ``know it when I see it'' definition
% of sufficient signs of deep learning in NLP
% % So before I jump in to my groups research, I would
% }
% \begin{frame}{}
% \begin{center}
% Why NLP is \structure{interesting} to deep learning (or not)
% \end{center}
% \begin{itemize}
% \item Language has rich latent structure.
% \begin{itemize}[]
% \item Although it seems to be surprisingly hard to write down.
% \end{itemize}
% \air
% \item Availability of tremendous amounts$^*$ of supervised data
% \begin{itemize}
% \item $^*$For some tasks, maybe not the right ones.
% \end{itemize}
% \air
% \item Low-level mechanism for real-world AI tasks
% \begin{itemize}
% \item Likely still several years off (would love to be wrong)
% \end{itemize}
% \end{itemize}
% \end{frame}
\begin{frame}{}
\begin{center}
What's ML aspects have defined NLP problems?
\end{center}
\begin{enumerate}
\air
\item Large, discrete input state spaces.
\begin{itemize}
\item Vocabulary sizes in $10,000 - 100,000$
\end{itemize}
\air
\item Long-term dependencies
\begin{itemize}
\item \textit{Sasha is giving a talk today at Oracle, $\ldots$, he is excited}.
\end{itemize}
\air
\item Variable-length output spaces
\begin{itemize}
\item e.g. sentences, documents, conversations
\end{itemize}
\air
\end{enumerate}
\end{frame}
\begin{frame}
\begin{block}{}
\begin{quote}
Although current deep learning research tends to claim to
encompass NLP, I'm (1) much less convinced about the strength of
the results, compared to the results in, say, vision ...
\end{quote}
\end{block}
\begin{flushright}
- Michael Jordan (2014) (quoted in Chris Manning, ``Computational Linguistics and Deep Learning'')
\end{flushright}
\end{frame}
\begin{frame}{}
\begin{center}
\structure{Sequence-to-Sequence} is pretty convincing
\end{center}
\begin{itemize}
\item Machine Translation \Cite{kalchbrenner2013recurrent,sutskever2014sequence, Cho2014, bahdanau2014neural,luong15effective}
\air
\item Question Answering \Cite{Hermann2015}
\item Sentence Compression \Cite{filippova15sentence}
\item Parsing \Cite{vinyals15grammar}
\item \textit{Summarization} \Cite{Rush2015}
\item Conversation \Cite{Vinyals2015}
\item Argument Generation \Cite{Wang}
\item \textit{Grammar Correction} \Cite{Schmaltz2016}
\item Speech \Cite{Chorowski2015}
\item Caption Generation \Cite{Xu2015}
\item Video-to-Text \Cite{Venugopalan2015}
\air
\end{itemize}
\end{frame}
\begin{frame}%{Deep Learning Toolbox}
\begin{center}
\alert{Seq2Seq Neural Network Toolbox}
\air
\end{center}
\begin{center}
\begin{tabular}{cclll}
\structure{Embeddings} & & sparse features &$\Rightarrow$& dense features \\\\
% \structure{Convolutions} && feature n-grams & $\Rightarrow$& dense features \\\\
\structure{RNNs} & & feature sequences & $\Rightarrow$ &dense features \\\\
\structure{Softmax} & & dense features & $\Rightarrow$ & discrete predictions \\
\end{tabular}
\end{center}
\end{frame}
% \note{
% Deep learning models for NLP, tend to be made up of three different components.
% Embeddings, which map sparse indicators features to dense low-dimensiosnal features,
% Convolutions, which map each contiguous n-gram of features in a sequence to
% dense features.
% and recurrent neural networks or RNNs which map a sequence of features to
% a dense feature representation.
% The entirety of this talk, will be made up of using these tools in different
% permutations, so I will start by talking about each in more detail}
\begin{frame}
\begin{center}
\begin{tabular}{cclll}
\structure{Embeddings} & & sparse features & $\Rightarrow$ & dense features \\\\
\end{tabular}
\end{center}
\begin{center}
\includegraphics[width=7cm]{emb}
\end{center}
\end{frame}
% \note{
% So first is the embedding, which maps from an indicator feature to a low-dimension representation.
% The most common of these has been for word embeddings, which I am sure you have all used. But there
% has also been interesting work on other feature embeddings for tasks like dependency parsing.
% }
\begin{frame}
\vspace{-5cm}
\hspace*{-2cm}
\includegraphics[width=1.5\textwidth]{graph}
\end{frame}
% \begin{frame}
% \begin{center}
% \begin{tabular}{cclll}
% % \structure{Embeddings} & & sparse features &$\Rightarrow$& dense features \\\\
% \structure{Convolutions} && feature n-grams & $\Rightarrow$& dense features \\\\
% % \structure{RNNs} & & feature sequences & $\Rightarrow$ &dense features \\
% \end{tabular}
% \end{center}
% \begin{center}
% \includegraphics[width=9cm]{conv}
% \end{center}
% \end{frame}
% \begin{frame}
% \air
% \includegraphics[width=\textwidth,trim={0 0 0 19.5cm},clip]{filters}
% \begin{center}
% \Cite{DBLP:conf/eccv/ZeilerF14}
% \end{center}
% \end{frame}
% \begin{frame}
% \begin{center}
% \includegraphics[width=9cm]{alpha}
% \end{center}
% \begin{center}
% \Cite{silver2016mastering}
% \end{center}
% \end{frame}
\begin{frame}
\begin{center}
\begin{tabular}{cclll}
\structure{RNNs/LSTMs} & & feature sequences & $\Rightarrow$ &dense features \\\\
\end{tabular}
\end{center}
\begin{center}
\includegraphics[width=11cm]{rnn}
\end{center}
% (In practice, LSTM update semantics are used for all these applications.)
% \begin{itemize}
% \item
% \end{itemize}
\end{frame}
% % \begin{frame}
% % \includegraphics[width=\textwidth]{good}
% % \caption{Xu et al (2015)}
% % \end{frame}
\begin{frame}
\begin{center}
\begin{tabular}{cclll}
\structure{Softmax} & & dense features & $\Rightarrow$ & discrete predictions \
\end{tabular}
\air
\includegraphics[width=0.8\textwidth]{rnnlm5}
\end{center}
\[ p(\wvec_t | \wvec_1, \ldots, \wvec_{t-1}; \theta) = \text{softmax}(\mathbf{W}_{out} \mathbf{h}_{t-1} + \mathbf{b}_{out}) \]
\[ p(\wvec_{1:T} ) = \prod_{t} p(\wvec_t | \wvec_1, \ldots, \wvec_{t-1}) \]
% \caption{Xu et al (2015)}
\end{frame}
\begin{frame}
\begin{center}
\structure{Contextual Language Model / ``seq2seq''}
\end{center}
\air
\begin{center}
\includegraphics[width=0.6\textwidth]{rnnlm6}
\end{center}
\begin{itemize}
\item Key idea, contextual language model based on encoder $\cvec$:
\end{itemize}
\[ p(\wvec_{1:T} | \cvec) = \prod_{t} p(\wvec_t | \wvec_1, \ldots, \wvec_{t-1}, \cvec) \]
\end{frame}
\begin{frame}
\begin{center}
Actual Seq2Seq / Encoder-Decoder / Attention-Based Models
\end{center}
\begin{center}
\includegraphics[width=0.7\textwidth]{simple-attn}
\end{center}
\begin{itemize}
\item Different encoders, attention mechanisms, input feeding, ...
\air
\item Almost all models use LSTMs or other gated RNNs
\air
\item Large multi-layer networks necessary for good performance.
\begin{itemize}
\item 4 layer, 1000 hidden dims is common for MT
\end{itemize}
% \item Main idea, contextual language model based on encoder $\cvec$:
\end{itemize}
% \[ p(\wvec_{1:T} | \cvec) = \prod_{t} p(\wvec_t | \wvec_1, \ldots, \wvec_{t-1}, \cvec) \]
\end{frame}
\begin{frame}
\centerline{\textbf{Seq2Seq Applications:} \alert{Sentence Summarization} \Cite{Rush2015} }
\begin{center}
\textbf{Source}
\end{center}
\begin{figure}
\textit{\structure{Russian Defense Minister Ivanov}
called \structure{Sunday} for the creation of
a joint front \structure{for combating} global terrorism. }
\end{figure}
\begin{center}
\textbf{Target}
\end{center}
\mair
\begin{figure}
\centering
\textit{\structure{Russia} calls for joint
front \structure{against} terrorism.}
\end{figure}
\air
\air
% \textbf{Summarization Phenomena:}
% \begin{itemize}
% \item<2-> \alert<2>{Generalization}
% \item<3-> \alert<3>{Deletion}
% \item<4-> \alert<4>{Paraphrase}
% % \item<5-> \alert<5>{Tense}
% \end{itemize}
\pause
\begin{itemize}
\item Used by The Washington Post to suggest headlines \Cite{shuguangwang}
\end{itemize}
\end{frame}
\begin{frame}
\centerline{\textbf{Seq2Seq Applications:} \alert{Grammar Correction} \Cite{Schmaltz2016} }
% \centerline{\structure{Grammar Correction} \cite{}}
\begin{center}
\textbf{Source}
\end{center}
\begin{figure}
\textit{There is no \structure{a doubt}, tracking \structure{systems has} brought many benefits in this information
age . }
\end{figure}
\begin{center}
\textbf{Target}
\end{center}
\mair
\begin{figure}
\centering
\textit{There is no doubt, tracking systems have
brought many benefits in this information
age . }
\end{figure}
\pause
\begin{itemize}
\item First-place on BEA 11 grammar correction shared task \Cite{Daudaravicius2016}
\end{itemize}
\end{frame}
\begin{frame}
\centerline{\textbf{Seq2Seq Applications:} \alert{Im2Latex} [In Submission] }
% \centerline{\structure{Grammar Correction} \cite{}}
\begin{center}
\textbf{Source}
\end{center}
\begin{figure}
\includegraphics[width=\textwidth]{math}
\end{figure}
\begin{center}
\textbf{Target}
\end{center}
\mair
\begin{figure}
\centering
\end{figure}
\centerline{\href{http://localhost:8900/}{[Latex Example]}}
\end{frame}
\begin{frame}
\centerline{\structure{This Talk}}
\air
\air
\begin{itemize}
\item How can we \textbf{interpret} these learned hidden representations?
\air
\item How should we \textbf{train} these style of models?
\air
\item How can we \textbf{shrink} these models for practical applications?
\end{itemize}
\end{frame}
\begin{frame}
\centerline{\structure{This Talk}}
\air
\air
\begin{itemize}
\item How can we \textbf{interpret} these learned hidden representations?
\begin{center}
\alert{LSTMVis}
\Cite{Strobelt2016}
\end{center}
\air
\item \textcolor{gray}{How should we \textbf{train} these style of models? \Cite{Wiseman2016a}}
\air
\item \textcolor{gray}{How can we \textbf{shrink} these models for practical applications? \Cite{Kim2016a}}
\end{itemize}
\end{frame}
\section{Interpretation}
\begin{frame}
\air
\includegraphics[width=\textwidth,trim={0 0 0 19.5cm},clip]{filters}
\begin{center}
\Cite{DBLP:conf/eccv/ZeilerF14}
\end{center}
\end{frame}
% \begin{frame}
% \begin{frame}
% \begin{center}
% \includegraphics[width=\textwidth]{lstm1}
% {\footnotesize (Karpathy et al, 2015)}
% \end{center}
% % \caption{Xu et al (2015)}
% \end{frame}
\begin{frame}
% \begin{frame}
\centerline{Vector-Space RNN Representation}
\begin{center}
\includegraphics[height=5cm]{lstmrep}
\begin{center}
\includegraphics[width=11cm]{rnn}
\end{center}
\end{center}
\end{frame}
\begin{frame}
% \begin{frame}
\begin{center}
\includegraphics[width=\textwidth]{lstm1}
\Cite{karpathy2015visualizing}
% {\footnotesize (Karpathy et al, 2015)}
\end{center}
% \caption{Xu et al (2015)}
\end{frame}
\begin{frame}
\centerline{\alert{Example 1}: Synthetic (Finite-State) Language}
\air
\begin{center}
\includegraphics[width=9cm]{parenlang}
\end{center}
\mair
\begin{itemize}
\item Numbers are randomly generated, must match nesting level.
\air
\item Train a predict-next-word language model (decoder-only).
\end{itemize}
\[ p(\wvec_t | \wvec_1, \ldots, \wvec_{t-1}) \]
\air
\centerline{\href{http://lstm.seas.harvard.edu/client/pattern_finder.html?data_set=00parens&source=states::states2&pos=150}{[Parens Example]}}
\end{frame}
\begin{frame}
\centerline{\alert{Example 2}: Real Language}
\air
\begin{description}
\item[alphabet:] all english words
\item[corpus:] Project Gutenberg Children's books
\end{description}
% \begin{center}
% \includegraphics[width=9cm]{parenlang}
% \end{center}
\begin{itemize}
% \item
% \air
\item Train a predict-next-word language model (decoder-only).
\end{itemize}
\[ p(\wvec_t | \wvec_1, \ldots, \wvec_{t-1}) \]
\air
\centerline{ \href{http://lstm.seas.harvard.edu/client/pattern_finder.html?data_set=05childbook&source=states::states1&pos=100}{[LM Example]}}
\end{frame}
\begin{frame}
\centerline{\alert{Example 3}: Seq2Seq Encoder}
\air
\begin{description}
\item[alphabet:] all english words
\item[corpus:] Summarization
\end{description}
% \begin{center}
% \includegraphics[width=9cm]{parenlang}
% \end{center}
\begin{itemize}
% \item
% \air
\item Train a full seq2seq model, examine \textit{encoder} LSTM.
\end{itemize}
\air
\centerline{ \href{http://lstm.seas.harvard.edu/client/pattern_finder.html?data_set=20autoencoder&source=states::states2&pos=100}{[Summarization Example]}}
\end{frame}
% \begin{frame}
% \centerline{LSTMVis: Next Steps}
% \begin{itemize}
% \item More models
% \air
% \item Further data annotations
% \air
% \item
% \end{itemize}
% % \begin{center}
% % \includegraphics{}
% % \end{center}
% \begin{itemize}
% \item
% \end{itemize}
% \end{frame}
\begin{frame}
\centerline{\structure{This Talk}}
\air
\air
\begin{itemize}
\item \textcolor{gray}{How can we \textbf{interpret} these learned hidden representations? \Cite{Strobelt2016}}
\air
\item How should we \textbf{train} these style of models?
\air
\begin{center}
\alert{Sequence-to-Sequence Learning as Beam-Search
Optimization}
\Cite{Wiseman2016a}
\end{center}
\air
\item \textcolor{gray}{ How can we \textbf{shrink} these models for practical applications \Cite{Kim2016a}? }
\end{itemize}
\end{frame}
\begin{frame}
\centerline{Some More Seq2Seq \alert{Details} }
\air
\air
Training Objective: Multiclass NLL (for training targets $y_{1:T}$)
\[ \text{NLL}(\theta) = -\sum_{t} \log p(\wvec_{t} = y_t | \wvec_{1:t-1} = y_{1:t-1}, \cvec; \theta) \]
\air
Test Objective: Structured output space
\[ \wvec^*_{1:T} = \argmax_{\wvec_{1:T}} \sum_{t} \log p(\wvec_{t} | \wvec_{1:t-1}, \cvec; \theta) \]
\pause
\begin{itemize}
\item Note: Completely intractable $O(\text{\#vocab} ^T)$
\end{itemize}
\end{frame}
\begin{frame}
\centerline{Standard Approach: \structure{Beam Search}}
\air
\begin{enumerate}
\item Start with $K$ partial starting hypotheses $\wvec^{(1:K)}$
\item For timesteps $t$ from $1$ to $T$:
\pause
\begin{enumerate}
\item Compute for all $k, \wvec_{t}$
\[s(\wvec_t, \wvec_{1:t-1}^{(k)}) \gets \log p(\wvec_{t} | \wvec^{(k)}_{1:t-1}, \cvec) + \log p(\wvec^{(k)}_{1:t-1}| \cvec) \]
\pause
\item Replace the $K$ highest scoring target sequences
\[\wvec_{1:t}^{(1:K)} \gets K\argmax_{\wvec_{1:t}} s(\wvec_t, \wvec_{1:t-1}^{(k)})\]
\end{enumerate}
\end{enumerate}
% \begin{enumerate}
% \item Start with $K$ partial starting hypotheses $\wvec^{(1:K)}$
% \item For timesteps $t$ from $1$ to $T$:
% \begin{enumerate}
% \item Compute for all $k, \wvec_{t}$
% \[s(\wvec_t, k) \gets \log p(\wvec_{t} | \wvec^{(k)}_{1:t-1}, \cvec; \theta) + \log p(\wvec^{(k)}_{1:t-1}| \cvec;\theta) \]
% \item Save $K$ highest scoring target sequences
% \[\wvec_{1:t+1}^{(1:K)} \gets K\arg\max_{\wvec_t, k} s(\wvec_t, k)\]
% \end{enumerate}
% \end{enumerate}
\pause
% \begin{itemize}
% \item Note: Requires computing $p(\wvec_{t} | \wvec^{(k)}_{1:t-1}, \cvec; \theta)$ for many $\wvec^{(k)}_{1:t-1}$
% \end{itemize}
\end{frame}
\begin{frame}[fragile]
\begin{center}
\structure{Beam Search Example} ($K=3$)
\end{center}
\air
\air
\air
\begin{center}
\begin{tikzpicture}[transform canvas = {scale=0.8}]
\tikzstyle{beam}=[draw, minimum height=0.6cm, anchor=base, text height=5, text depth=0, minimum width=1.5cm,thin, rounded corners, line width=0.03cm]
\tikzstyle{mat}=[draw=white]
\tikzset{>=stealth',every on chain/.append style={join},
every join/.style={->}}
\begin{scope}
\matrix (G) [matrix of nodes, nodes={beam},inner sep=1mm,row sep=0.03cm, column sep=0.8cm ] {
\node<1->(G-1-1){a}; & \node<2->(G-1-2){red}; & \node<3->(G-1-3){dog}; & \node<4->(G-1-4){smells}; & \node<5->(G-1-5){home}; & \node<6->(G-1-6){today}; \\
\node<1->(G-2-1){the}; & \node<2->(G-2-2){dog}; & \node<3->(G-2-3){dog}; & \node<4->(G-2-4){barks}; & \node<5->(G-2-5){quickly}; & \node<6->(G-2-6){Friday}; \\
\node<1->(G-3-1){red}; & \node<2->(G-3-2){blue}; & \node<3->(G-3-3){cat}; & \node<4->(G-3-4){barks}; & \node<5->(G-3-5){straight}; & \node<6->(G-3-6){now}; \\ };
\only<2->{
\draw[->] (G-1-1.east) -> (G-1-2.west);
\draw[->] (G-2-1.east) -> (G-2-2.west);
\draw[->] (G-1-1.east) -> (G-3-2.west);
\draw[double, line width=0.03cm] (G-3-1.south west) -- (G-3-2.south east);
}
\only<3->{
\draw[->] (G-1-2.east) -> (G-2-3.west);
\draw[->] (G-3-2.east) -> (G-3-3.west);
\draw[->] (G-3-2.east) -> (G-1-3.west);
\draw[double, line width=0.03cm] (G-3-1.south west) -- (G-3-3.south east);
}
\only<4->{
\draw[->] (G-1-3.east) -> (G-3-4.west);
\draw[->] (G-2-3.east) -> (G-2-4.west);
\draw[->] (G-1-3.east) -> (G-1-4.west);
\draw[double, line width=0.03cm] (G-3-1.south west) -- (G-3-4.south east);
}
\only<6->{
\draw[->] (G-1-5.east) -> (G-1-6.west);
\draw[->] (G-1-5.east) -> (G-3-6.west);
\draw[->] (G-2-5.east) -> (G-2-6.west);
\draw[double, line width=0.03cm] (G-3-1.south west) -- (G-3-6.south east);
}
\only<5->{
\draw[->] (G-3-4.east) -> (G-1-5.west);
\draw[->] (G-3-4.east) -> (G-2-5.west);
\draw[->] (G-3-4.east) -> (G-3-5.west);
\draw[double, line width=0.03cm] (G-3-1.south west) -- (G-3-5.south east);
}
\end{scope}
\end{tikzpicture}
\end{center}
\air
For timesteps $t$ from $1$ to $T$:
\begin{enumerate}
\item Compute for all $k, \wvec_{t}$
\[s(\wvec_t, \wvec_{1:t-1}^{(k)}) \gets \log p(\wvec_{t} | \wvec^{(k)}_{1:t-1}, \cvec) + \log p(\wvec^{(k)}_{1:t-1}| \cvec) \]
\item Replace the $K$ highest scoring target sequences
\[\wvec_{1:t}^{(1:K)} \gets K\argmax_{\wvec_{1:t}} s(\wvec_t, \wvec_{1:t-1}^{(k)})\]
\end{enumerate}
\end{frame}
\begin{frame}
\centerline{Theoretical \alert{Issues} with Standard Setup}