# Utterance Rewriting with Contrastive Learning in Multi-turn Dialogue

arXiv: https://arxiv.org/abs/2203.11587 · HTML: https://mlsanity.com/paper/2203.11587/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Few-Shot Generative Conversational Query Rewriting](https://mlsanity.com/paper/2006.05009/) (2020)
- [Improving Multi-turn Dialogue Modelling with Utterance ReWriter](https://mlsanity.com/paper/1906.07004/) (2019)
- [GECOR: An End-to-End Generative Ellipsis and Co-reference Resolution Model for Task-Oriented Dialogue](https://mlsanity.com/paper/1909.12086/) (2019)
- [Incomplete Utterance Rewriting as Semantic Segmentation](https://mlsanity.com/paper/2009.13166/) (2020)
- [SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration](https://mlsanity.com/paper/2008.01474/) (2020)
- [Semantic Role Labeling Guided Multi-turn Dialogue ReWriter](https://mlsanity.com/paper/2010.01417/) (2020)
- [Unsupervised Context Rewriting for Open Domain Conversation](https://mlsanity.com/paper/1910.08282/) (2019)
- [CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues](https://mlsanity.com/paper/2105.09914/) (2021)
- [Advances in Multi-turn Dialogue Comprehension: A Survey](https://mlsanity.com/paper/2103.03125/) (2021)
- [Robust Dialogue Utterance Rewriting as Sequence Tagging](https://mlsanity.com/paper/2012.14535/) (2020)
- [Conversational Query Rewriting with Self-supervised Learning](https://mlsanity.com/paper/2102.04708/) (2021)
- [A Deep Neural Network for Chinese Zero Pronoun Resolution](https://mlsanity.com/paper/1604.05800/) (2016)

## Built on by (most cited first, 1 of 1)

- [Incomplete Utterance Rewriting as Sequential Greedy Tagging](https://mlsanity.com/paper/2307.06337/) (2023)

## Abstract (arXiv)

Context modeling plays a significant role in building multi-turn dialogue systems. In order to make full use of context information, systems can use Incomplete Utterance Rewriting(IUR) methods to simplify the multi-turn dialogue into single-turn by merging current utterance and context information into a self-contained utterance. However, previous approaches ignore the intent consistency between the original query and rewritten query. The detection of omitted or coreferred locations in the original query can be further improved. In this paper, we introduce contrastive learning and multi-task learning to jointly model the problem. Our method benefits from carefully designed self-supervised objectives, which act as auxiliary tasks to capture semantics at both sentence-level and token-level. The experiments show that our proposed model achieves state-of-the-art performance on several public datasets.