Papers by Yixuan Meng
Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization (2023.emnlp-main)
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| Challenge: | Existing methods for biomedical named entity recognition require laborious human effort. |
| Approach: | They propose a Synonym Generalization framework that recognizes biomedical concepts using span-based predictions. |
| Outcome: | The proposed framework outperforms dictionary-based approaches on a wide range of benchmarks. |
Few-Shot Table-to-Text Generation with Prototype Memory (2021.findings-emnlp)
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| Challenge: | Neural table-to-text generation models are data-hungry and require large amounts of training data to learn the mapping between tables and texts. |
| Approach: | They propose a framework for table-to-text generation under the few-shot scenario that uses retrieved prototypes and a prototype selector to bridge the structural gap between tables and texts. |
| Outcome: | The proposed framework significantly improves the model performance on three benchmark datasets with state-of-the-art models. |
Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models (2022.acl-long)
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| Challenge: | Despite the growing progress of probing knowledge for pre-trained language models, specialised areas such as the biomedical domain are vastly under-explored. |
| Approach: | They propose a biomedical knowledge probing benchmark, MedLAMA, constructed based on the Unified Medical Language System (UMLS) Metathesaurus. |
| Outcome: | The proposed approach pushes the acc@10 to 28%, but the performance gap remains notable. |
Explanation-based Finetuning Makes Models More Robust to Spurious Cues (2023.acl-long)
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Josh Magnus Ludan, Yixuan Meng, Tai Nguyen, Saurabh Shah, Qing Lyu, Marianna Apidianaki, Chris Callison-Burch
| Challenge: | Large Language Models (LLMs) learn correlations between labels and features that are irrelevant to the task, leading to poor generalization on out-of-distribution data. |
| Approach: | They propose an explanation-based approach to fine tune large language models to generate a free-text explanation supporting their answer. |
| Outcome: | The proposed model is more robust against spurious cues in terms of accuracy drop across four classification tasks: ComVE (+1.2), CREAK (+9.1), e-SNLI (+5.4), and SBIC (+6.5). |
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning (2022.findings-naacl)
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| Challenge: | Existing pre-trained MLMs produce an anisotropic distribution of token representations . this is not ideal for tasks that require discriminative semantic meanings of distinct tokens - a problem that exists in pre-training models . |
| Approach: | They propose a continual pre-training approach that encourages BERT to learn an isotropic distribution of token representations. |
| Outcome: | The proposed approach improves on a wide range of English and Chinese benchmarks. |