Retrieving relevant answers from heterogeneous data formats, for given for questions, is a challenging problem. The proposed models leverage the superior predictive power of pretrained transformer models like RoBERTa, ALBERT and ELECTRA, to identify the most relevant information in an associated passage for the next agent turn. To further enhance the performance, we have explored fine-tuning models on different question answering datasets like SQuAD2.0 and Natural Questions (NQ) corpus before using ensemble techniques for combining multiple models. Our team SB_NITK ranked 6th on the leaderboard for the Knowledge Identification task, and our best ensemble model achieved an Exact score of 58.58 and an F1 score of 73.39.

Ensemble ALBERT and RoBERTa for Span Prediction in Question Answering

Document-grounded Dialogue and Conversational QA

workshop paper

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Ensemble ALBERT and RoBERTa for Span Prediction in Question Answering

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