Papers by Hanwen Zha
HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data (2020.findings-emnlp)
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| Challenge: | Existing question answering datasets focus on dealing with homogeneous information, but using homogenous information alone might lead to coverage problems. |
| Approach: | They propose a large-scale question-answering dataset that requires reasoning on heterogeneous information. |
| Outcome: | The proposed model can achieve an EM score of 40% while the existing model is far behind human performance. |
Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs? (2024.naacl-long)
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| Challenge: | Existing large language models lack knowledge of nuanced, domain-specific details and are susceptible to hallucinations. |
| Approach: | They construct a benchmark that measures head, torso, and tail facts in terms of popularity. |
| Outcome: | The proposed model is based on 18K question-answer pairs regarding head, torso, and tail facts in terms of popularity. |
Global Textual Relation Embedding for Relational Understanding (P19-1)
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| Challenge: | Existing methods to learn textual relation embeddings are lacking in large open-domain corpora. |
| Approach: | They propose to learn a general-purpose embedding of textual relations using a large dataset from Freebase. |
| Outcome: | The proposed embedding can facilitate downstream tasks requiring relational understanding of the text. |
Logic2Text: High-Fidelity Natural Language Generation from Logical Forms (2020.findings-emnlp)
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| Challenge: | Recent studies on Natural Language Generation (NLG) from structured data focus on surface descriptions of simple record sequences, for example, attribute-value pairs of fixed or very limited schema. |
| Approach: | They propose to use a large-scale dataset to generate NLG from logical forms to obtain controllable and faithful generations from structured data. |
| Outcome: | The proposed model can describe interesting facts from logical inferences across records, but it is difficult to produce such fidelity. |