Papers by Michael Vu
Toward Implicit Reference in Dialog: A Survey of Methods and Data (2022.aacl-main)
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| Challenge: | In natural language, speakers often leave out information that is understood by the other party through the shared context. |
| Approach: | They propose to use omitted entities as implicit references in dialogs to improve language processing. |
| Outcome: | The proposed method is based on a set of experiments which show that the proposed method has a high level of accuracy and is a success. |
An Analysis of Multilingual FActScore (2024.emnlp-main)
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| Challenge: | Recent advances in LLMs have demonstrated significant capabilities in many applications. |
| Approach: | They propose a dataset for FActScore on texts generated by strong multilingual LLMs and evaluate their performance in other languages. |
| Outcome: | The proposed dataset shows that LLMs exhibit distinct behaviors in fact extraction and fact scoring tasks. |
Spatial Layouts in News Homepages Capture Human Preferences (2025.emnlp-main)
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| Challenge: | Homepage layouts are daily and manually curated by expert human news editors, and serve as a proxy for information prioritization. |
| Approach: | They propose a pairwise preference model to capture ranked preferences between news items . they use a dataset of 3,000 news homepages captured twice daily over a five-year period . |
| Outcome: | The proposed model infers editorial preference with over 0.7 F1 score (based on human trials) it also ranks local city council policies passed over ten years in San Francisco, evaluating their "newsworthiness". |
ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents (2020.acl-demos)
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Chia-Yu Li, Daniel Ortega, Dirk Väth, Florian Lux, Lindsey Vanderlyn, Maximilian Schmidt, Michael Neumann, Moritz Völkel, Pavel Denisov, Sabrina Jenne, Zorica Kacarevic, Ngoc Thang Vu
| Challenge: | Existing toolkits for developing dialog systems are limited to core components and do not support multi-modal processing and social signals. |
| Approach: | They propose to use ADVISER to develop multi-modal dialog agents using multi-text and social signals. |
| Outcome: | The proposed toolkit is flexible, easy to use, and easy to extend for linguists and cognitive scientists, thereby providing a flexible platform for collaborative research. |