Papers by Yingxue Zhang

4 papers
Retrieval-Augmented Machine Translation with Unstructured Knowledge (2025.findings-emnlp)

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Challenge: Retrieval-augmented generation (RAG) is a new approach to enhance large language models (LLMs).
Approach: They propose a multi-task training method to teach LLMs how to use information from multilingual documents during their translation.
Outcome: The proposed method improves LLMs by 1.6-3.1 BLEU and 1.0-2.0 COMET scores in En-Zh, and 1.7-2.9 BLUE and 2.1-2.7 COMET score in En de.
WeTS: A Benchmark for Translation Suggestion (2022.emnlp-main)

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Challenge: Existing studies focus on overall performance of machine translation but ignore TS performance, authors say . if TS is applied into post-editing, it will reduce the time and cost of post-production.
Approach: They propose to use a golden corpus annotated by experts to generate a translation suggestion model.
Outcome: The proposed model improves on the golden corpus annotated by translators on four translation directions.
Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition (2021.naacl-main)

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Challenge: Existing models fail to fully utilize contextual information which plays an important role in interpreting sentences.
Approach: They propose a graph-based Context Tracking Network to model the discourse context for IDRR.
Outcome: The proposed model can integrate sentence-level and token-level contextual semantics better than existing models.
EWEK-QA : Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems (2024.acl-long)

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Challenge: citation-based QA systems are suffering from two shortcomings . they usually rely only on web as a source of extracted knowledge and external knowledge sources can hamper the efficiency of the system.
Approach: They propose to use a web-based knowledge graph retrieval solution to enrich extracted knowledge fed to a citation-based QA system.
Outcome: The proposed model outperforms open-source state-of-the-art models in 7 quantitative and human evaluation tasks.

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