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

arXiv: https://arxiv.org/abs/1909.12086 · HTML: https://mlsanity.com/paper/1909.12086/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 30 of 33)

- [DialogSum: A Real-Life Scenario Dialogue Summarization Dataset](https://mlsanity.com/paper/2105.06762/) (2021)
- [Can Large Language Models Understand Context?](https://mlsanity.com/paper/2402.00858/) (2024)
- [Semantic Representation for Dialogue Modeling](https://mlsanity.com/paper/2105.10188/) (2021)
- [DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://mlsanity.com/paper/2307.10172/) (2023)
- [INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning](https://mlsanity.com/paper/2401.06532/) (2024)
- [CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues](https://mlsanity.com/paper/2105.09914/) (2021)
- [Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL](https://mlsanity.com/paper/2106.02282/) (2021)
- [Know Thy Strengths: Comprehensive Dialogue State Tracking Diagnostics](https://mlsanity.com/paper/2112.08321/) (2021)
- [Enhance Incomplete Utterance Restoration by Joint Learning Token Extraction and Text Generation](https://mlsanity.com/paper/2204.03958/) (2022)
- [UniDU: Towards A Unified Generative Dialogue Understanding Framework](https://mlsanity.com/paper/2204.04637/) (2022)
- [Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting](https://mlsanity.com/paper/2307.00866/) (2023)
- [Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation](https://mlsanity.com/paper/2205.13928/) (2022)
- [Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction](https://mlsanity.com/paper/2410.18481/) (2024)
- [VD-PCR: Improving Visual Dialog with Pronoun Coreference Resolution](https://mlsanity.com/paper/2205.14693/) (2022)
- [McQueen: a Benchmark for Multimodal Conversational Query Rewrite](https://mlsanity.com/paper/2210.12775/) (2022)
- [Resolving References in Visually-Grounded Dialogue via Text Generation](https://mlsanity.com/paper/2309.13430/) (2023)
- [Improving Bot Response Contradiction Detection via Utterance Rewriting](https://mlsanity.com/paper/2207.11862/) (2022)
- [DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning](https://mlsanity.com/paper/2205.12662/) (2022)
- [Online Coreference Resolution for Dialogue Processing: Improving Mention-Linking on Real-Time Conversations](https://mlsanity.com/paper/2205.10670/) (2022)
- [Incomplete Utterance Rewriting as Sequential Greedy Tagging](https://mlsanity.com/paper/2307.06337/) (2023)
- [Utterance Rewriting with Contrastive Learning in Multi-turn Dialogue](https://mlsanity.com/paper/2203.11587/) (2022)
- [Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems](https://mlsanity.com/paper/2307.07255/) (2023)
- [MARRS: Multimodal Reference Resolution System](https://mlsanity.com/paper/2311.01650/) (2023)
- [Incomplete Utterance Rewriting with Editing Operation Guidance and Utterance Augmentation](https://mlsanity.com/paper/2503.16043/) (2025)
- [Coreference Augmentation for Multi-Domain Task-Oriented Dialogue State Tracking](https://mlsanity.com/paper/2106.08723/) (2021)
- [Action based Network for Conversation Question Reformulation](https://mlsanity.com/paper/2111.14445/) (2021)
- [Active Learning and Multi-label Classification for Ellipsis and Coreference Detection in Conversational Question-Answering](https://mlsanity.com/paper/2207.03145/) (2022)
- [5IDER: Unified Query Rewriting for Steering, Intent Carryover, Disfluencies, Entity Carryover and Repair](https://mlsanity.com/paper/2306.01855/) (2023)
- [Exploration of Marker-Based Approaches in Argument Mining through Augmented Natural Language](https://mlsanity.com/paper/2406.08606/) (2024)
- [In-Context Learning with Reinforcement Learning for Incomplete Utterance Rewriting](https://mlsanity.com/paper/2408.13028/) (2024)

## Abstract (arXiv)

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.