# Incomplete Utterance Rewriting as Semantic Segmentation

arXiv: https://arxiv.org/abs/2009.13166 · HTML: https://mlsanity.com/paper/2009.13166/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

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

- [Document-level Relation Extraction as Semantic Segmentation](https://mlsanity.com/paper/2106.03618/) (2021)
- [Text Generation with Text-Editing Models](https://mlsanity.com/paper/2206.07043/) (2022)
- [Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL](https://mlsanity.com/paper/2106.02282/) (2021)
- [Enhance Incomplete Utterance Restoration by Joint Learning Token Extraction and Text Generation](https://mlsanity.com/paper/2204.03958/) (2022)
- [Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting](https://mlsanity.com/paper/2307.00866/) (2023)
- [Conversational Query Rewriting with Self-supervised Learning](https://mlsanity.com/paper/2102.04708/) (2021)
- [Hierarchical Context Tagging for Utterance Rewriting](https://mlsanity.com/paper/2206.11218/) (2022)
- [DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning](https://mlsanity.com/paper/2205.12662/) (2022)
- [Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions](https://mlsanity.com/paper/2210.09894/) (2022)
- [DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection](https://mlsanity.com/paper/2210.11279/) (2022)
- [Contextualized Semantic Distance between Highly Overlapped Texts](https://mlsanity.com/paper/2110.01176/) (2021)
- [Self-Attention for Incomplete Utterance Rewriting](https://mlsanity.com/paper/2202.12160/) (2022)
- [Incomplete Utterance Rewriting as Sequential Greedy Tagging](https://mlsanity.com/paper/2307.06337/) (2023)
- [Multi-Granularity Information Interaction Framework for Incomplete Utterance Rewriting](https://mlsanity.com/paper/2312.11945/) (2023)
- [Machine Translation Testing via Syntactic Tree Pruning](https://mlsanity.com/paper/2401.00751/) (2024)
- [Utterance Rewriting with Contrastive Learning in Multi-turn Dialogue](https://mlsanity.com/paper/2203.11587/) (2022)
- [QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2305.06655/) (2023)
- [Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems](https://mlsanity.com/paper/2307.07255/) (2023)
- [Few-Shot Adaptation for Parsing Contextual Utterances with LLMs](https://mlsanity.com/paper/2309.10168/) (2023)
- [On the Robustness of Question Rewriting Systems to Questions of Varying Hardness](https://mlsanity.com/paper/2311.06807/) (2023)
- [Incomplete Utterance Rewriting with Editing Operation Guidance and Utterance Augmentation](https://mlsanity.com/paper/2503.16043/) (2025)
- [Unsupervised Full Constituency Parsing with Neighboring Distribution Divergence](https://mlsanity.com/paper/2110.15931/) (2021)
- [Action based Network for Conversation Question Reformulation](https://mlsanity.com/paper/2111.14445/) (2021)
- [Zero-shot Cross-lingual Conversational Semantic Role Labeling](https://mlsanity.com/paper/2204.04914/) (2022)
- [Pretrained Language Encoders are Natural Tagging Frameworks for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2208.09617/) (2022)
- [Meeting Decision Tracker: Making Meeting Minutes with De-Contextualized Utterances](https://mlsanity.com/paper/2210.11374/) (2022)
- [HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction](https://mlsanity.com/paper/2305.04297/) (2023)
- [Making a Long Story Short in Conversation Modeling](https://mlsanity.com/paper/2402.00143/) (2024)
- [CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite](https://mlsanity.com/paper/2403.11873/) (2024)
- [In-Context Learning with Reinforcement Learning for Incomplete Utterance Rewriting](https://mlsanity.com/paper/2408.13028/) (2024)

## Abstract (arXiv)

Recent years the task of incomplete utterance rewriting has raised a large attention. Previous works usually shape it as a machine translation task and employ sequence to sequence based architecture with copy mechanism. In this paper, we present a novel and extensive approach, which formulates it as a semantic segmentation task. Instead of generating from scratch, such a formulation introduces edit operations and shapes the problem as prediction of a word-level edit matrix. Benefiting from being able to capture both local and global information, our approach achieves state-of-the-art performance on several public datasets. Furthermore, our approach is four times faster than the standard approach in inference.