論 文Papers

CONFERENCE (INTERNATIONAL)

Arc Loss: Softmax with Additive Angular Margin for Answer Retrieval

Rikiya Suzuki (Waseda univ.), Sumio Fujita, Tetsuya Sakai (Waseda univ.)

The 15th Asia Information Retrieval Societies Conference (AIRS 2019), 2019/11

Category:

自然言語処理 (Natural Language Processing) 情報検索 (Information Retrieval) 機械学習 (Machine Learning)

Abstract:
Answer retrieval is a crucial step in question answering. To determine the best Q–A pair in a candidate pool, traditional approaches adopt triplet loss (i.e., pairwise ranking loss) for a meaningful distributed representation. Triplet loss is widely used to push away a negative answer from a certain question in a feature space and leads to a better understanding of the relationship between questions and answers. However, triplet loss is inefficient because it requires two steps: triplet generation and negative sampling. In this study, we propose an alternative objective loss function, namely, arc loss, for more efficient and effective learning than that by triplet loss. We evaluate the proposed approach on a commonly used QA dataset and demonstrate that it significantly outperforms the triplet loss baseline.