Papers by Thuy Vu
CDA: a Cost Efficient Content-based Multilingual Web Document Aligner (2021.eacl-main)
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| Challenge: | a Content-based document alignment approach is an efficient way to align multilingual web documents based on content. |
| Approach: | They propose a Content-based document alignment approach to align multilingual web documents based on content in parallel training data for machine translation systems. |
| Outcome: | The proposed method achieves comparable performance with state-of-the-art systems in the WMT-16 Bilingual Document Alignment Shared Task benchmark while operating in multilingual space. |
Question-Answer Sentence Graph for Joint Modeling Answer Selection (2023.eacl-main)
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| Challenge: | Existing approaches to automate Question Answering (QA) are graph-based and can target large text databases. |
| Approach: | They propose graph-based approaches for Answer Sentence Selection (AS2) . they train and integrate state-of-the-art (SOTA) models for computing scores . |
| Outcome: | The proposed approach outperforms baseline models on academic benchmarks and a real-world dataset on unseen queries. |
Joint Models for Answer Verification in Question Answering Systems (2021.acl-long)
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| Challenge: | Using a joint approach, we found that the model is more efficient than those developed in machine reading (MR) work. |
| Approach: | They propose a joint model for selecting correct answer sentences among the top k provided by answer sentence selection modules. |
| Outcome: | The proposed model improves on WikiQA, TREC-QA, and a real-world dataset. |
Double Retrieval and Ranking for Accurate Question Answering (2023.findings-eacl)
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| Challenge: | Recent work shows that answer verification models can improve the state of the art in Question Answering . despite the fact that the supporting candidates are ranked only according to the relevancy with the question, the model still lacks the support needed for other answer candidates. |
| Approach: | They propose a double reranking model that selects the best support for each target answer . they propose 'second neural retrieval stage' to encode question and answer pair as query . |
| Outcome: | The proposed approach improves the state of the art in Question Answering . the proposed model ranked candidates according to relevancy and not the answer . but the proposed approach fails to provide the best support . |
AVA: an Automatic eValuation Approach for Question Answering Systems (2021.naacl-main)
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| Challenge: | AVA is an automatic evaluation approach for question answering . it uses transformer-based language models to encode question, answer, and reference texts . |
| Approach: | They propose an automatic evaluation approach for Question Answering that uses Transformer-based language models to encode question, answer, and reference texts. |
| Outcome: | AVA can estimate system Accuracy with an error lower than 7% at 95% confidence level . the proposed approach achieves 74.7% F1 score in predicting human judgment for single answers . |
FocusQA: Open-Domain Question Answering with a Context in Focus (2022.findings-emnlp)
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Gianni Barlacchi, Ivano Lauriola, Alessandro Moschitti, Marco Del Tredici, Xiaoyu Shen, Thuy Vu, Bill Byrne, Adrià de Gispert
| Challenge: | a new method for question answering with a context in focus simulates a free interaction with QA systems. |
| Approach: | They introduce question answering with a cotext in focus task that simulates a free interaction with QA systems. |
| Outcome: | The proposed model outperforms state-of-the-art models for question answering with a context in focus up to 21.3% absolute points. |
Retrieving Support to Rank Answers in Open-Domain Question Answering (2025.emnlp-main)
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| Challenge: | a novel question answering architecture retrieves content relevant to the combined pair . previous work on automatic claim verification has shown hallucinations . |
| Approach: | They propose a question-answer architecture that prioritizes supporting evidence . it retrieves paragraphs that directly substantiate the correctness of a with respect to q . |
| Outcome: | The proposed approach can be used by large language models to retrieve explanatory paragraphs that ground their reasoning. |
Reference-based Weak Supervision for Answer Sentence Selection using Web Data (2021.findings-emnlp)
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| Challenge: | Existing solutions for QA use weakly-supervised data from Web. |
| Approach: | They propose a data pipeline that harvests weakly-supervised answer sentences from Web data . they train TANDA models, which are the state of the art for AS2 . |
| Outcome: | The proposed pipeline improves on three different datasets and sets the state-of-the-art models to P@1=90.1% and MAP=92.9%. |
CypherSmith: Transforming Text-to-Cypher Generation for LLMs with Synthetic Data (2026.acl-long)
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| Challenge: | Existing datasets are small, domain-limited, and lack diversity, constraining LLM progress. |
| Approach: | They propose a knowledge Graph retrieval tool that can translate natural language questions into structured queries. |
| Outcome: | Extensive experiments show that CypherSmith achieves state-of-the-art performance. |