Biaoyan Fang


2022

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What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text
Biaoyan Fang | Timothy Baldwin | Karin Verspoor
Findings of the Association for Computational Linguistics: ACL 2022

Procedural text contains rich anaphoric phenomena, yet has not received much attention in NLP. To fill this gap, we investigate the textual properties of two types of procedural text, recipes and chemical patents, and generalize an anaphora annotation framework developed for the chemical domain for modeling anaphoric phenomena in recipes. We apply this framework to annotate the RecipeRef corpus with both bridging and coreference relations. Through comparison to chemical patents, we show the complexity of anaphora resolution in recipes. We demonstrate empirically that transfer learning from the chemical domain improves resolution of anaphora in recipes, suggesting transferability of general procedural knowledge.

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Context-Aware Sentence Classification in Evidence-Based Medicine
Biaoyan Fang | Fajri Koto
Proceedings of the The 20th Annual Workshop of the Australasian Language Technology Association

2021

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ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain
Biaoyan Fang | Christian Druckenbrodt | Saber A Akhondi | Jiayuan He | Timothy Baldwin | Karin Verspoor
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Chemical patents contain rich coreference and bridging links, which are the target of this research. Specially, we introduce a novel annotation scheme, based on which we create the ChEMU-Ref dataset from reaction description snippets in English-language chemical patents. We propose a neural approach to anaphora resolution, which we show to achieve strong results, especially when jointly trained over coreference and bridging links.

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Handling Variance of Pretrained Language Models in Grading Evidence in the Medical Literature
Fajri Koto | Biaoyan Fang
Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association

In this paper, we investigate the utility of modern pretrained language models for the evidence grading system in the medical literature based on the ALTA 2021 shared task. We benchmark 1) domain-specific models that are optimized for medical literature and 2) domain-generic models with rich latent discourse representation (i.e. ELECTRA, RoBERTa). Our empirical experiments reveal that these modern pretrained language models suffer from high variance, and the ensemble method can improve the model performance. We found that ELECTRA performs best with an accuracy of 53.6% on the test set, outperforming domain-specific models.1