Abstract

Ellipsis and co-reference are common and ubiquitous especially in multi-turn dialogues. In this paper, we treat the resolution of ellipsis and co-reference in dialogue as a problem of generating omitted or referred expressions from the dialogue context. We therefore propose a unified end-to-end Generative Ellipsis and CO-reference Resolution model (GECOR) in the context of dialogue. The model can generate a new pragmatically complete user utterance by alternating the generation and copy mode for each user utterance. A multi-task learning framework is further proposed to integrate the GECOR into an end-to-end task-oriented dialogue. In order to train both the GECOR and the multi-task learning framework, we manually construct a new dataset on the basis of the public dataset CamRest676 with both ellipsis and co-reference annotation. On this dataset, intrinsic evaluations on the resolution of ellipsis and co-reference show that the GECOR model significantly outperforms the sequence-to-sequence (seq2seq) baseline model in terms of EM, BLEU and F1 while extrinsic evaluations on the downstream dialogue task demonstrate that our multi-task learning framework with GECOR achieves a higher success rate of task completion than TSCP, a state-of-the-art end-to-end task-oriented dialogue model.

Highlights

  • Due to the rhetorical principle of saving words and avoiding repetitions, ellipsis and co-reference occur frequently in multi-turn dialogues leaving utterances paragmatically incomplete if they are separate from context

  • If user utterances can be automatically supplemented with information that is left out or substituted by anaphora according to the dialogue context as humans do

  • The essential idea behind generative ellipsis and co-reference resolution model (GECOR) is that we treat the resolution of ellipsis and co-reference in user utterances as a generation task: transforming a user utterance with ellipsis or anaphora into a new utterance where the left-out or referred expressions are automatically generated from the dialogue context

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Summary

Introduction

Due to the rhetorical principle of saving words and avoiding repetitions, ellipsis and co-reference occur frequently in multi-turn dialogues leaving utterances paragmatically incomplete if they are separate from context. In order to achieve this goal, we propose an endto-end generative ellipsis and co-reference resolution model (GECOR) for task-oriented dialogue in this paper. The essential idea behind GECOR is that we treat the resolution of ellipsis and co-reference in user utterances as a generation task: transforming a user utterance with ellipsis or anaphora into a new utterance where the left-out or referred expressions are automatically generated from the dialogue context. We use an endto-end sequence-to-sequence model with two encoders for this transformation task, where one encoder reads the user utterance and the other the dialogue context and the decoder generates the complete utterance. We construct a new dataset based on CamRest676 for ellipsis and co-reference resolution in the context of task-oriented dialogue.

Related Work
Ellipsis and Co-Reference Resolution Reformulation
Model Structure
Task-Oriented Dialogue with GECOR
Data Annotation for Ellipsis and Co-Reference Rosultion in Dialogue
Evaluation Metrics
Parameter Settings
Baselines and Comparisons
The GECOR Model
The Multi-Task Learning Model
Conclusion and Future Work

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