Papers by Wenyang Hui
Recall, Expand, and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing (2023.acl-long)
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| Challenge: | State-of-the-art (SOTA) methods use the cross-encoder architecture to concatenate a mention (and its context) with each type and feed it into a pretrained language model (PLM) to score their relevance. |
| Approach: | They propose to perform entity typing in a recall-expand-filter manner and use a novel model to encode and score all these K candidates in one forward pass. |
| Outcome: | The proposed method is thousands of times faster than the CE-based architecture and is very efficient in fine-grained (130 types) and coarse-grain (9 types) entity typing. |
Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset (2023.findings-emnlp)
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| Challenge: | Existing benchmarks or datasets require only a few steps of reasoning, making it difficult to analyse AI’s behaviour with reference to different problems within a specific topic in detail. |
| Approach: | They propose a conic10K math problem dataset that requires only a few steps of reasoning to be analysed. |
| Outcome: | The proposed dataset shows that existing language models exhibit weak performance on complex reasoning. |