Analyzing COVID-related Stance and Arguments using BERT-based Natural Language Inference
Kamila Alibaeva, Natalia Loukachevitch · Computational Linguistics and Intellectual Technologies · 2022
In this paper we present our approach for stance detection and premise classification from COVID-related messages developed for the RuArg-2022 evaluation. The methods are based on so-called NLI-setting (natural language inference) of BERT-based text classification (Sun et al., 2019), when the input of a model includes two sentences: a target sentence and a conclusion (for example, positive to masks). We also use translating Russian messages to English, which allows us to leverage COVID-trained BERT model. Besides, we use additional marking techniques of targeted entities. Our approach achieved the best results on both RuArg-2022 tasks. We also studied the contribution of marking techniques across datasets, tasks, models and languages of RuArg evaluation. We found that " keyword ” gave the highest average increase over corresponding basic methods.