Papers › Context-Aware Transformer Pre-Training for Answer Sentence Selection
Context-Aware Transformer Pre-Training for Answer Sentence Selection
Luca Di Liello, Siddhant Garg, Alessandro Moschitti
Answer Sentence Selection (AS2) is a core component for building an accurate Question Answering pipeline. AS2 models rank a set of candidate sentences based on how likely they answer a given question. The state of the art in AS2 exploits pre-trained transformers by transferring them on large annotated datasets, while using local contextual information around the candidate sentence. In this paper, we propose three pre-training objectives designed to mimic the downstream fine-tuning task of contextual AS2. This allows for specializing LMs when fine-tuning for contextual AS2. Our experiments on three public and two large-scale industrial datasets show that our pre-training approaches (applied to RoBERTa and ELECTRA) can improve baseline contextual AS2 accuracy by up to 8% on some datasets.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | TrecQA | Contextual DeBERTa-V3-Large + SSP | MAP | 0.919 | #4 of 13 | Archive leaderboard | report |
| Question Answering | TrecQA | Contextual DeBERTa-V3-Large + SSP | MRR | 0.945 | #4 of 13 | Archive leaderboard | report |
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