Papers by Zhiyu Cao
Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to rewrite ambiguous queries ignore feedback from query rewriting, passage retrieval and response generation in the rewritten process. |
| Approach: | They propose to construct self-consistent preference alignment data to generate more diverse rewritten queries. |
| Outcome: | The proposed method is effective in both in- and out-of-distribution scenarios. |
Discourse Coherence and Response-Guided Context Rewriting for Multi-Party Dialogue Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing research on multi-party dialogue generation has focused on structural information inherent in dialogues, but colloquial expressions and incomplete utterances often impede comprehension and weaken the fidelity of dialogue structure representations. |
| Approach: | They propose a framework to improve multi-party dialogue generation through dialogue context rewriting using two complementary feedback signals to construct preference data for both context & response generation. |
| Outcome: | The proposed framework improves multi-party dialogue generation through dialogue context rewriting. |
Incomplete Utterance Rewriting with Editing Operation Guidance and Utterance Augmentation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing generation methods on Incomplete Utterance Rewriting (IUR) can generate coherent utterances, but they often include irrelevant and redundant tokens in rewritten utteras . |
| Approach: | They propose a multi-task learning framework that uses editing operation labels to guide generation model to focus on critical tokens in dialogue context. |
| Outcome: | The proposed model outperforms state-of-the-art models on open-domain and task-oriented dialogues on three datasets. |
ICR: Iterative Clarification and Rewriting for Conversational Search (2025.emnlp-main)
Copied to clipboard
| Challenge: | Conversational Query Rewriting (CQR) is a key step in conversational question answering . it aims to rewrite vague queries into de-contextualized queries, thereby promoting conversational search. |
| Approach: | They propose an iterative rewriting scheme that pivots on clarification questions . they propose to rewrite queries into de-contextualized queries to promote conversational search . |
| Outcome: | The proposed framework improves retrieval performance on two popular datasets. |
Two-stage Incomplete Utterance Rewriting on Editing Operation (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods to generate rewritten utterances based on dialogue context ignore coreference and ellipsis in dialogues. |
| Approach: | They propose a framework where the first stage generates editing operations and the second stage rewrites incomplete utterances utilizing the generated editing operations. |
| Outcome: | The proposed framework outperforms the existing models on three IUR datasets. |