# MLR: A Two-stage Conversational Query Rewriting Model with Multi-task Learning

arXiv: https://arxiv.org/abs/2004.05812 · HTML: https://mlsanity.com/paper/2004.05812/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [Enhance Incomplete Utterance Restoration by Joint Learning Token Extraction and Text Generation](https://mlsanity.com/paper/2204.03958/) (2022)
- [McQueen: a Benchmark for Multimodal Conversational Query Rewrite](https://mlsanity.com/paper/2210.12775/) (2022)
- [Conversational Query Rewriting with Self-supervised Learning](https://mlsanity.com/paper/2102.04708/) (2021)
- [Emotional Support with LLM-based Empathetic Dialogue Generation](https://mlsanity.com/paper/2507.12820/) (2025)
- [Few-Shot Adaptation for Parsing Contextual Utterances with LLMs](https://mlsanity.com/paper/2309.10168/) (2023)
- [CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite](https://mlsanity.com/paper/2403.11873/) (2024)

## Abstract (arXiv)

Conversational context understanding aims to recognize the real intention of user from the conversation history, which is critical for building the dialogue system. However, the multi-turn conversation understanding in open domain is still quite challenging, which requires the system extracting the important information and resolving the dependencies in contexts among a variety of open topics. In this paper, we propose the conversational query rewriting model - MLR, which is a Multi-task model on sequence Labeling and query Rewriting. MLR reformulates the multi-turn conversational queries into a single turn query, which conveys the true intention of users concisely and alleviates the difficulty of the multi-turn dialogue modeling. In the model, we formulate the query rewriting as a sequence generation problem and introduce word category information via the auxiliary word category label predicting task. To train our model, we construct a new Chinese query rewriting dataset and conduct experiments on it. The experimental results show that our model outperforms compared models, and prove the effectiveness of the word category information in improving the rewriting performance.