Abstract
In this work, we present our approach for solving the SemEval 2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC). The task is a sentence pair classification problem where the goal is to detect whether a given word common to both the sentences evokes the same meaning. We submit systems for both the settings - Multilingual (the pair’s sentences belong to the same language) and Cross-Lingual (the pair’s sentences belong to different languages). The training data is provided only in English. Consequently, we employ cross-lingual transfer techniques. Our approach employs fine-tuning pre-trained transformer-based language models, like ELECTRA and ALBERT, for the English task and XLM-R for all other tasks. To improve these systems’ performance, we propose adding a signal to the word to be disambiguated and augmenting our data by sentence pair reversal. We further augment the dataset provided to us with WiC, XL-WiC and SemCor 3.0. Using ensembles, we achieve strong performance in the Multilingual task, placing first in the EN-EN and FR-FR sub-tasks. For the Cross-Lingual setting, we employed translate-test methods and a zero-shot method, using our multilingual models, with the latter performing slightly better.- Anthology ID:
- 2021.semeval-1.62
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Alexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 511–520
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.62
- DOI:
- 10.18653/v1/2021.semeval-1.62
- Cite (ACL):
- Rohan Gupta, Jay Mundra, Deepak Mahajan, and Ashutosh Modi. 2021. MCL@IITK at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation using Augmented Data, Signals, and Transformers. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 511–520, Online. Association for Computational Linguistics.
- Cite (Informal):
- MCL@IITK at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation using Augmented Data, Signals, and Transformers (Gupta et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.semeval-1.62.pdf
- Data
- WiC, Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison, XL-WiC