{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:38:35Z","timestamp":1701218315790},"reference-count":20,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2022,10,1]]},"DOI":"10.1587\/transinf.2021edp7227","type":"journal-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T22:33:36Z","timestamp":1664577216000},"page":"1780-1789","source":"Crossref","is-referenced-by-count":0,"title":["Analysis on Norms of Word Embedding and Hidden Vectors in Neural Conversational Model Based on Encoder-Decoder RNN"],"prefix":"10.1587","volume":"E105.D","author":[{"given":"Manaya","family":"TOMIOKA","sequence":"first","affiliation":[{"name":"Graduate School of Science and Engineering, Doshisha University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tsuneo","family":"KATO","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Engineering, Doshisha University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akihiro","family":"TAMURA","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Engineering, Doshisha University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] T. Mikolov, M. Karafiat, L. Burget, J. Cernocky, and S. Khudanpur, \u201cRecurrent neural network based language model,\u201d Proc. Interspeech 2010, pp.1045-1048, 2010. 10.21437\/Interspeech.2010-343","DOI":"10.21437\/Interspeech.2010-343"},{"key":"2","unstructured":"[2] I. Sutskever, O. Vinyals, and Q.V. Le, \u201cSequence to sequence learning with neural networks,\u201d arXiv preprint, arXiv:1409.3215, 2014. 10.48550\/arXiv.1409.3215"},{"key":"3","unstructured":"[3] D. Bahdanau, K. Cho, and Y. Bengio, \u201cNeural machine translation by jointly learning to align and translate,\u201d arXiv preprint, arXiv:1409.0473, 2014. 10.48550\/arXiv.1409.0473"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] T. Luong, H. Pham, and C.D. Manning, \u201cEffective approaches to attention-based neural machine translation,\u201d Proc. 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.1412-1421, 2015. 10.18653\/v1\/D15-1166","DOI":"10.18653\/v1\/D15-1166"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] I.V. Serban, A. Sordoni, Y. Bengio, A. Courville, and J. Pineau, \u201cBuilding end-to-end dialogue systems using generative hierarchical neural network models,\u201d Proc. 30th AAAI Conference on Artifi. Intelli., (AAAI'16), pp.3776-3783, 2016. 10.1609\/aaai.v30i1.9883","DOI":"10.1609\/aaai.v30i1.9883"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] I.V. Serban, A. Sordoni, R. Lowe, L. Charlin, J. Pineau, A. Courville, and Y. Bengio, \u201cA hierarchical latent variable encoder-decoder model for generating dialogue,\u201d Proc. 31st AAAI Conference on Artifi. Intelli., (AAAI'17), pp.3295-3301, 2017. 10.1609\/aaai.v31i1.10983","DOI":"10.1609\/aaai.v31i1.10983"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] J. Li, M. Galley, C. Brockett, J. Gao, and B. Dolan, \u201cA diversity-promoting objective function for neural conversation models,\u201d Proc. 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pp.110-119, 2016. 10.18653\/v1\/N16-1014","DOI":"10.18653\/v1\/N16-1014"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] J. Li, W. Monroe, A. Ritter, D. Jurafsky, M. Galley, and J. Gao, \u201cDeep reinforcement learning for dialogue generation,\u201d Proc. 2016 Conference on Empirical Methods in Natural Language Processing, pp.1192-1202, Nov. 2016. 10.18653\/v1\/D16-1127","DOI":"10.18653\/v1\/D16-1127"},{"key":"9","unstructured":"[9] Y. Zhang, M. Galley, J. Gao, Z. Gan, X. Li, C. Brockett, and B. Dolan, \u201cGenerating informative and diverse conversational responses via adversarial information maximization,\u201d Proc. 32nd Int. Conf. Neural Information Processing Systems, NIPS'18, pp.1815-1825, 2018. 10.48550\/arXiv.1809.05972"},{"key":"10","unstructured":"[10] Y. Zhang, X. Gao, S. Lee, C. Brockett, M. Galley, J. Gao, and B. Dolan, \u201cImproving response generation consistency via contrastive learning,\u201d Proc. 22nd Annual Meeting of Special Interest Group on Discourse and Dialogue (SIGDIAL), pp.133-146, 2021."},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] T. Zhao and T. Kawahara, \u201cMulti-referenced training for dialogue response generation,\u201d Proc. 22nd Annual Meeting of Special Interest Group on Discourse and Dialogue (SIGDIAL), pp.204-215, 2021. 10.48550\/arXiv.2009.07117","DOI":"10.18653\/v1\/2021.sigdial-1.20"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] Z. Tu, Y. Liu, Z. Lu, X. Liu, and H. Li, \u201cContext gates for neural machine translation,\u201d Transactions of the Association for Computational Linguistics, vol.5, pp.87-99, 2017. 10.1162\/tacl_a_00048","DOI":"10.1162\/tacl_a_00048"},{"key":"13","unstructured":"[13] H. Ghader and C. Monz, \u201cWhat does attention in neural machine translation pay attention to?,\u201d Proc. Eighth International Joint Conference on Natural Language Processing, pp.30-39, 2017. 10.48550\/arXiv.1710.03348"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] G. Tang, R. Sennrich, and J. Nivre, \u201cAn analysis of attention mechanisms: The case of word sense disambiguation in neural machine translation,\u201d Proc. Third Conference on Machine Translation: Research Papers, pp.26-35, 2018. 10.18653\/v1\/W18-6304","DOI":"10.18653\/v1\/W18-6304"},{"key":"15","unstructured":"[15] S. Jain and B.C. Wallace, \u201cAttention is not explanation,\u201d Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pp.3543-3556, 2019. 10.48550\/arXiv.1902.10186"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] K. Cho, B. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, \u201cLearning phrase representations using RNN encoder-decoder for statistical machine translation,\u201d Proc. 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.1724-1734, 2014. 10.3115\/v1\/D14-1179","DOI":"10.3115\/v1\/D14-1179"},{"key":"17","unstructured":"[17] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L.u. Kaiser, and I. Polosukhin, \u201cAttention is all you need,\u201d Proc. 31st International Conference in Neural Information Processing Systems, pp.5998-6008, 2017. 10.48550\/arXiv.1706.03762"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] G. Kobayashi, T. Kuribayashi, S. Yokoi, and K. Inui, \u201cAttention is not only a weight: Analyzing transformers with vector norms,\u201d Proc. 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.7057-7075, 2020. 10.18653\/v1\/2020.emnlp-main.574","DOI":"10.18653\/v1\/2020.emnlp-main.574"},{"key":"19","unstructured":"[19] T. Kudo, K. Yamamoto, and M. Yuji, \u201cApplying conditional random fields to Japanese morphological analysis,\u201d Proc. 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.230-237, 2004."},{"key":"20","unstructured":"[20] A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi, \u201cThe curious case of neural text degeneration,\u201d arXiv preprint, arXiv:1904.09751, 2020. 10.48550\/arXiv.1904.09751"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E105.D\/10\/E105.D_2021EDP7227\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T07:59:29Z","timestamp":1701158369000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E105.D\/10\/E105.D_2021EDP7227\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,1]]},"references-count":20,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2022]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2021edp7227","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,1]]},"article-number":"2021EDP7227"}}