mlsanityData to 14 Oct 2026

IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back Translation

arXiv 2102.12777 ↗First posted 25 Feb 2021cs.CLcited by 1

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Abstract (arXiv)

This paper introduces our systems for all three subtasks of SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning. To help our model better represent and understand abstract concepts in natural language, we well-design many simple and effective approaches adapted to the backbone model (RoBERTa). Specifically, we formalize the subtasks into the multiple-choice question answering format and add special tokens to abstract concepts, then, the final prediction of question answering is considered as the result of subtasks. Additionally, we employ many finetuning tricks to improve the performance. Experimental results show that our approaches achieve significant performance compared with the baseline systems. Our approaches achieve eighth rank on subtask-1 and tenth rank on subtask-2.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 24 references that are in the map. First posted on arXiv 25 Feb 2021.

Builds on

19 of 24 references.

References · 19

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7 more
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Citing papers per year: 2021: 1.

1 paper.

  1. 2021SemEval-2021 Task 4: Reading Comprehension of Abstract Meaningcited by 3