cltl-dialogueclassification

Description

Detects dialogue acts in texts and annotates the signals with the dialogue act labels and scores. The annotations are pushed to the event bus and can be taken up for further processing.

We implemented two dialogue act classifiers:

1. Deberta fine-tuned with the SILICONE data set:

Based on: https://huggingface.co/diwank/silicone-deberta-pair

2XLM-RoBERTa fine-tuned with the MIDAS data set:

Based on: https://github.com/DianDYu/MIDAS_dialog_act

Getting started

Prerequisites

This repository uses Python >= 3.9

Be sure to run in a virtual python environment (e.g. conda, venv, mkvirtualenv, etc.)

Installation

1. In the root directory of this repo run

   pip install -e .

2. Download the fine-tuned XLM-roberta from:

https://vu.data.surfsara.nl/index.php/s/dw0YCJAVFM870DT

and put the files in the directory:

resources/midas-da-xlmroberta

Usage

To apply this to conversations stored in EMISSOR format:

```bash
python3 examples/annotato_emissor_conversation_with_emotions.py --emissor "../data/emissor" --model "../resources/midas-da-xlmroberta" --model-name midas --scenario "14a1c27d-dfd2-465b-9ab2-90e9ea91d214"
```

For using this repository as a package different project and on a different virtual environment, you may

• install a published version from PyPI:

  pip install cltl.dialogue_act_classification

• or, for the latest snapshot, run:

  pip install git+git://github.com/leolani/cltl-dialogueclassification.git@main

Then you can import it in a python script as:

import cltl.dialogue_act_classification

To test the classifier run:

PYTHONPATH=src python -m unittest

References:

• Chapuis, Emile, Pierre Colombo, Matteo Manica, Matthieu Labeau, and Chloe Clavel. "Hierarchical pre-training for sequence labelling in spoken dialog." arXiv preprint arXiv:2009.11152 (2020).
• Yu, Dian, and Zhou Yu. "Midas: A dialog act annotation scheme for open domain human machine spoken conversations." arXiv preprint arXiv:1908.10023 (2019).
• Santamaría, Selene Báez, Thomas Baier, Taewoon Kim, Lea Krause, Jaap Kruijt, and Piek Vossen. "EMISSOR: A platform for capturing multimodal interactions as Episodic Memories and Interpretations with Situated Scenario-based Ontological References." In Proceedings of the 1st Workshop on Multimodal Semantic Representations (MMSR), pp. 56-77. 2021.

Integration in the Leolani event-bus

Can be integrated in the event-bus and to generate annotations in EMISSOR through a service.py that is included. In the configuration file of the event-bus,the input and output topics need to specified as well as the emotion detectors.