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2020 | OriginalPaper | Chapter

Arc Loss: Softmax with Additive Angular Margin for Answer Retrieval

Authors : Rikiya Suzuki, Sumio Fujita, Tetsuya Sakai

Published in: Information Retrieval Technology

Publisher: Springer International Publishing

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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.

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previous chapter A Semantic Expansion-Based Joint Model for Answer Ranking in Chinese Question Answering Systems

next chapter Context-Aware Collaborative Ranking

https://github.com/shuzi/insuranceQA.

https://nlp.stanford.edu/projects/glove/.

Bachrach, Y., et al.: An attention mechanism for neural answer selection using a combined global and local view. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 425–432 (2017)

Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: Additive angular margin loss for deep face recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019

Dos Santos, C., Tan, M., Xiang, B., Zhou, B.: Attentive pooling networks (2016)

Dos Santos, C.N., Zadrozny, B.: Learning character-level representations for part-of-speech tagging. In: Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32 ICML 2014, pp. II-1818-II-1826. JMLR.org (2014)

Feng, M., Xiang, B., Glass, M.R., Wang, L., Zhou, B.: Applying deep learning to answer selection: a study and an open task. In: 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 813–820. IEEE (2015)

Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735CrossRef

Masala, M., Ruseti, S., Rebedea, T.: Sentence selection with neural networks using string kernels. Procedia Comput. Sci. 112(C), 1774–1782 (2017). https://doi.org/10.1016/j.procs.2017.08.209CrossRef

Park, D.H., Chang, Y.: Adversarial sampling and training for semi-supervised information retrieval. In: The World Wide Web Conference WWW 2019, pp. 1443–1453. ACM, New York, USA (2019). https://doi.org/10.1145/3308558.3313416

Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

10.

Sakai, T.: Laboratory Experiments in Information Retrieval. TIRS, vol. 40. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-1199-4CrossRefMATH

11.

Sakai, T., Ishikawa, D., Kando, N., Seki, Y., Kuriyama, K., Lin, C.Y.: Using graded-relevance metrics for evaluating community QA answer selection. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining WSDM 2011, pp. 187–196. ACM, New York, USA (2011). https://doi.org/10.1145/1935826.1935864

12.

Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR 2015, pp. 815–823. Boston, MA, USA, 7–12 June 2015. (2015). https://doi.org/10.1109/CVPR.2015.7298682

13.

Tan, M., dos Santos, C., Xiang, B., Zhou, B.: Improved representation learning for question answer matching. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 464–473. Association for Computational Linguistics, Berlin, Germany, Aug 2016. https://doi.org/10.18653/v1/P16-1044

14.

Tan, M., Santos, C.d., Xiang, B., Zhou, B.: LSTM-based deep learning models for non-factoid answer selection (2015)

15.

Tran, N.K., Niederée, C.: Multihop attention networks for question answer matching. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval SIGIR 2018, pp. 325–334. ACM, New York, NY, USA (2018). https://doi.org/10.1145/3209978.3210009

16.

Tran, N.K., Niederée, C.: A neural network-based framework for non-factoid question answering. In: Companion Proceedings of the The Web Conference 2018, WWW 2018, pp. 1979–1983. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2018). https://doi.org/10.1145/3184558.3191830

17.

Wang, B., Liu, K., Zhao, J.: Inner attention based recurrent neural networks for answer selection. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1288–1297. Association for Computational Linguistics, Berlin, Germany (Aug 2016). https://doi.org/10.18653/v1/P16-1122

18.

Wang, J., et al.: IRGAN: A minimax game for unifying generative and discriminative information retrieval models. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval SIGIR 2017, pp. 515–524. ACM, New York, NY, USA (2017). https://doi.org/10.1145/3077136.3080786

Title: Arc Loss: Softmax with Additive Angular Margin for Answer Retrieval
Authors: Rikiya Suzuki
Sumio Fujita
Tetsuya Sakai
Publisher: Springer International Publishing
Book: Information Retrieval Technology
Print ISBN: 978-3-030-42834-1

Electronic ISBN: 978-3-030-42835-8

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-42835-8_4

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