Cambridge at SemEval-2021 Task 2: Neural WiC-Model with Data Augmentation and Exploration of Representation
Zheng Yuan, David Strohmaier · 2021
This paper describes the system of the Cambridge team submitted to the SemEval-2021 shared task on Multilingual and Cross-lingual Word-in-Context Disambiguation.Building on top of a pre-trained masked language model, our system is first pre-trained on out-ofdomain data, and then fine-tuned on in-domain data.We demonstrate the effectiveness of the proposed two-step training strategy and the benefits of data augmentation from both existing examples and new resources.We further investigate different representations and show that the addition of distance-based features is helpful in the word-in-context disambiguation task.Our system yields highly competitive results in the cross-lingual track without training on any cross-lingual data; and achieves state-of-the-art results in the multilingual track, ranking first in two languages (Arabic and Russian) and second in French out of 171 submitted systems.