IITK@LCP at SemEval 2021 Task 1: Classification for Lexical Complexity Regression Task
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Abstract (arXiv)
This paper describes our contribution to SemEval 2021 Task 1: Lexical Complexity Prediction. In our approach, we leverage the ELECTRA model and attempt to mirror the data annotation scheme. Although the task is a regression task, we show that we can treat it as an aggregation of several classification and regression models. This somewhat counter-intuitive approach achieved an MAE score of 0.0654 for Sub-Task 1 and MAE of 0.0811 on Sub-Task 2. Additionally, we used the concept of weak supervision signals from Gloss-BERT in our work, and it significantly improved the MAE score in Sub-Task 1.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 28 references that are in the map. First posted on arXiv 2 Apr 2021.
Builds on
8 of 28 references.
References · 8
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,935
- 2017All-but-the-Top: Simple and Effective Postprocessing for Word Representationscited by 205
- 2019GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledgecited by 78
- 2018FRAGE: Frequency-Agnostic Word Representationcited by 47
- 2018A Report on the Complex Word Identification Shared Task 2018cited by 30
- 2020CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Datacited by 24
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1.
3 papers.