A Self Consistency Based Reranking for Narrative Question Answering
Narrative question answering (NQA) is a challenging task in natural language processing that requires models to understand long textual contexts, capture relationships across events, and generate coherent responses. Despite recent advances in pretrained language models, most existing approaches rely on a single decoding output during inference, making them sensitive to generation variability and often resulting in incomplete or inconsistent answers .To address this limitation, we propose a self-ensemble Self-Consistency-Based reranking framework for narrative question answering. The proposed method generates multiple candidate answers for each story-question pair and selects the final ans...
| Authors | Molham Mohamed, Ali Hamdi |
|---|---|
| Primary category | cs.CL |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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