@inproceedings{94655d012ad44381bb4cd65d7d9f69a5,
title = "Arc Loss: Softmax with Additive Angular Margin for Answer Retrieval",
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 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.",
keywords = "Answer retrieval, Question answering, Representation learning",
author = "Rikiya Suzuki and Sumio Fujita and Tetsuya Sakai",
year = "2020",
doi = "10.1007/978-3-030-42835-8_4",
language = "English",
isbn = "9783030428341",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "34--40",
editor = "Wang, {Fu Lee} and Haoran Xie and Wai Lam and Aixin Sun and Lun-Wei Ku and Tianyong Hao and Wei Chen and Tak-Lam Wong and Xiaohui Tao",
booktitle = "Information Retrieval Technology - 15th Asia Information Retrieval Societies Conference, AIRS 2019, Proceedings",
note = "15th Asia Information Retrieval Societies Conference, AIRS 2019 ; Conference date: 07-11-2019 Through 09-11-2019",
}