Papers by Wang-Chiew Tan
OpinionDigest: A Simple Framework for Opinion Summarization (2020.acl-main)
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| Challenge: | Abstractive opinion summarization framework outperforms competitors' summarizing frameworks . extractive approaches produce well-formed text, but selecting the most popular opinions is challenging . |
| Approach: | They propose an abstractive opinion summarization framework that trains a Transformer model to reconstruct reviews from extracted opinions. |
| Outcome: | The proposed framework outperforms baselines on Yelp and shows promising customization capabilities. |
Convex Aggregation for Opinion Summarization (2021.findings-emnlp)
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| Challenge: | Recent advances in text autoencoders have significantly improved the quality of the latent space, allowing models to generate consistent text from aggregated latent vectors. |
| Approach: | They develop a framework which searches input-output word overlap for latent vector aggregation. |
| Outcome: | The proposed framework improves the quality of the latent space and establishes state-of-the-art performance on two opinion summarization benchmarks. |
Essentia: Mining Domain-specific Paraphrases with Word-Alignment Graphs (D19-53)
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| Challenge: | Existing methods for mining general-purpose paraphrases are often based on statistical methods, but domain-specific corpora are too small to fit statistical methods. |
| Approach: | They propose a method to mine paraphrases from a small set of sentences that roughly share the same topic or intent. |
| Outcome: | The proposed method obtains high quality paraphrases as evaluated by crowd workers. |
TimelineQA: A Benchmark for Question Answering over Timelines (2023.findings-acl)
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Wang-Chiew Tan, Jane Dwivedi-Yu, Yuliang Li, Lambert Mathias, Marzieh Saeidi, Jing Nathan Yan, Alon Halevy
| Challenge: | Existing question answering techniques for lifelogs do not provide accurate answers . augmented reality glasses have led to the creation of personal assistants . |
| Approach: | They propose to use a benchmark to query lifelogs to find out what happened in real life . they find that extractive QA systems out-perform retrieval-augmented QA techniques . |
| Outcome: | The proposed method outperforms state-of-the-art retrieval-augmented QA systems in atomic queries and multi-hop queries. |
SubjQA: A Dataset for Subjectivity and Review Comprehension (2020.emnlp-main)
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| Challenge: | Subjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified. |
| Approach: | They develop a dataset which investigates subjectivity in question answering . they find that subjectivity is an important feature in the case of QA . |
| Outcome: | The proposed dataset shows that subjectivity is an important feature in question answering (QA) it also shows that subjective questions and answers can have more complex interactions than previously thought. |
HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments (L18-1)
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Akari Asai, Sara Evensen, Behzad Golshan, Alon Halevy, Vivian Li, Andrei Lopatenko, Daniela Stepanov, Yoshihiko Suhara, Wang-Chiew Tan, Yinzhan Xu
| Challenge: | Recent research has focused on developing technologies that help users incorporate the findings of the science of happiness into their daily lives. |
| Approach: | They crowd-sourced HappyDB, a corpus of 100,000 happy moments, and applied several state-of-the-art analysis techniques to analyze HappyDB. |
| Outcome: | The proposed technology can understand how people express their happy moments in text and analyze them using state-of-the-art techniques. |
Open Information Extraction from Question-Answer Pairs (N19-1)
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| Challenge: | Existing work on OpenIE extracts structured data from sentences . a system for extracting tuples from question-answer pairs solves this problem . |
| Approach: | They propose a system for extracting tuples from question-answer pairs . they use distributed representations of a question and an answer to generate knowledge facts . |
| Outcome: | The proposed system extracts meaningful structured tuples from question-answer pairs . it can find new and interesting facts to extend knowledge bases, the authors show . |
Reimagining Retrieval Augmented Language Models for Answering Queries (2023.findings-acl)
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Wang-Chiew Tan, Yuliang Li, Pedro Rodriguez, Richard James, Xi Victoria Lin, Alon Halevy, Wen-tau Yih
| Challenge: | Large language models (LLMs) are expensive to train, deploy, and maintain, both financially and in terms of environmental impact. |
| Approach: | They present a reality check on large language models and compare their predictions to retrieval-augmented language models. |
| Outcome: | The proposed models fare better on question answering tasks and have become the foundation of impressive demos like Chat-GPT. |