  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.
