GECOR: An End-to-End Generative Ellipsis and Co-reference Resolution Model for Task-Oriented Dialogue

Source code and dataset for the paper "GECOR: An End-to-End Generative Ellipsis and Co-reference Resolution Model for Task-Oriented Dialogue" (Jun Quan, Deyi Xiong, Bonnie Webber and Changjian Hu. EMNLP, 2019)

Data

Since there are no publicly available labeled data for the resolution of ellipsis and co-reference in dialogue, we manually annotate such a new dataset based on the public dataset CamRest676 from the restaurant domain. This dataset contains the following json files:

1. CamRest676_annotated.json: the new annotated dataset for generative ellipsis and co-reference resolution research for task-oriented dialogue.
2. CamRest676.json: the original woz dialogue dataset, which contains the conversion from users and wizards, as well as a set of coarse labels for each user turn.
3. CamRestDB.json: the Cambridge restaurant database file, containing restaurants in the Cambridge UK area and a set of attributes.
4. CamRestOTGY.json: specific all the values the three informable slots can take.

Our annotation ———— CamRest676_annotated.json

Date statistics

The CamRest676 dataset contains 676 dialogues, with 2,744 user utterances. After annotation, 1,174 ellipsis versions and 1,209 co-reference versions are created from the 2,744 user utterances. 1,331 incomplete utterances are created that they are an either ellipsis or co-reference version. 1,413 of the 2,744 user utterances are complete and not amenable to change. No new versions are created from these 1,413 utterances.

Dataset Split for Experiments

We split the new dataset into a training set (accounting for 80%) and validation set (accounting for 20%) which can be used for the stand-alone ellipsis/coreference resolution task and the multi-task learning of both the ellipsis/co-reference resolution and end-to-end task-oriented dialogue.

Annotation Specification

Annotation cases for user utterances can be summarized into the following three conventions:

• If a user utterance contains an ellipsis or anaphor, we manually resolve the ambiguity of ellipsis or anaphor and supplement the user utterance with a correct expression by checking the dialogue context. In doing so, we create a pragmatically complete version for the utterance. If the utterance only contains an ellipsis and the ellipsis can be replaced with an anaphor, we create a co-reference version for it. Similarly, if the utterance only contains an anaphor and the anaphor can be omitted, we create an ellipsis version for the utterance.
• If the user utterance itself is pragmatically complete, without any ellipsis or anaphora, we create an anaphor and ellipsis version for it if such a creation is appropriate.
• If the utterance itself is complete and it is not suitable to create an ellipsis or anaphor version, we just do nothing. In CamRest676_annotated.json, the key 'transcript' represent the origin user utterance, and the keys 'transcript_complete', 'transcript_with_ellipsis', 'transcript_with_coreference' respectively represent the complete version, ellipsis version, coreference version of the user utterance after our annotation.

Data License

We make the new annotated dataset under the following licenses:

• Attribution 4.0 International (CC BY 4.0) license. (License URL: https://creativecommons.org/licenses/by/4.0/)

Code

GECOR Model and Task-Oriented Dialogue with GECOR.

Requirements

python 3.6
numpy==1.14.3
nltk==3.2.1
torch==0.3.1

pip install -r requirements.txt

Download pretrained Glove word vectors and place them in PROJECT_ROOT/data/glove.

Train

python model.py -mode train -model gecor-camrest

To train and test with different version of data (ellipsis/ co-reference/ mixed):
please modify the code in config.py and reader.py

To switch between the GECOR1 mode and GECOR2 mode:
please modify the code in GECOR_model.py

Test

python model.py -mode test -model gecor-camrest

References

If you use the source codes or datasets included here in your work, please cite the corresponding papers. The bibtex are listed below:

@inproceedings{quan2019gecor,
  title={GECOR: An End-to-End Generative Ellipsis and Co-reference Resolution Model for Task-Oriented Dialogue},
  author={Quan, Jun and Xiong, Deyi and Webber, Bonnie and Hu, Changjian},
  booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  pages={4539--4549},
  year={2019}
}