Non-Factoid型質問のための結論と理由で構成される回答文の生成手法
Saved in:
| Published in | 人工知能学会論文誌 Vol. 37; no. 2; pp. A-L64_1 - 9 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | Japanese |
| Published |
一般社団法人 人工知能学会
01.03.2022
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Author | 八島, 浩文 中辻, 真 |
|---|---|
| Author_xml | – sequence: 1 fullname: 中辻, 真 organization: NTT レゾナント株式会社 – sequence: 2 fullname: 八島, 浩文 organization: NTT レゾナント株式会社 |
| BookMark | eNo9kE9LAkEAxYcwyMx7X2JsZmd2ZjyKZAVLXerQaZj9V7uYxu5eOq6H_rBBHtRLoEejKAjpEw3u-jFSky7v9-A93uHtglKn2_EA2Meohk2DHyRhrIIa4dCQDWgxugXKmFAGBSKotPGIY7oDqnEc2Ahhg1CMzDK4PO12YEs5STdw5-NsMXufD190-qXTie6lS1P89BcfI52-Ff37YvCt02k-zfLHvk6Huvese9n8dVx8DvLRw6o8mCyj_CnLZ8M9sO2rduxVN6yAi9bhefMYWmdHJ82GBUMsmIJUuAIzSpFvE9fk3EZ1h2PhY8NGXNnM8xV2ETGFKQgxfIWw59UZrdvct5nLFamA1t9uGCfqypO3UXCjojupoiRw2p5cfyMJl8ZK1mzI5Uf_BedaRTJU5Bf4in3x |
| ContentType | Journal Article |
| Copyright | 人工知能学会2022 |
| Copyright_xml | – notice: 人工知能学会2022 |
| DOI | 10.1527/tjsai.37-2_A-L64 |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1346-8030 |
| EndPage | 9 |
| ExternalDocumentID | article_tjsai_37_2_37_37_2_A_L64_article_char_ja |
| GroupedDBID | 123 2WC ACGFS ALMA_UNASSIGNED_HOLDINGS CS3 E3Z EBS EJD JSF KQ8 OK1 PQQKQ RJT XSB |
| ID | FETCH-LOGICAL-j186a-48d816440fb3d577b09c718f12b07ab6efa1d035858332fa01ee9649b7fb6d7a3 |
| ISSN | 1346-0714 |
| IngestDate | Sun Jul 28 05:37:15 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 2 |
| Language | Japanese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-j186a-48d816440fb3d577b09c718f12b07ab6efa1d035858332fa01ee9649b7fb6d7a3 |
| OpenAccessLink | https://www.jstage.jst.go.jp/article/tjsai/37/2/37_37-2_A-L64/_article/-char/ja |
| ParticipantIDs | jstage_primary_article_tjsai_37_2_37_37_2_A_L64_article_char_ja |
| PublicationCentury | 2000 |
| PublicationDate | 2022/03/01 |
| PublicationDateYYYYMMDD | 2022-03-01 |
| PublicationDate_xml | – month: 03 year: 2022 text: 2022/03/01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | 人工知能学会論文誌 |
| PublicationYear | 2022 |
| Publisher | 一般社団法人 人工知能学会 |
| Publisher_xml | – name: 一般社団法人 人工知能学会 |
| References | [Nakatsuji 20a] Nakatsuji, M. and Okui, S.: Answer Generation through Unified Memories over Multiple Passages, in Proc. IJ-CAI’20, pp. 3823–3829 (2020) [Rajpurkar 16] Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P.: SQuAD: 100, 000+ Questions for Machine Comprehension of Text, CoRR, Vol. abs/1606.05250, (2016) [Ghosh 16] Ghosh, S., Vinyals, O., Strope, B., Roy, S., Dean, T., and Heck, L.: Contextual LSTM (CLSTM) Models for Large Scale NLP Tasks, CoRR, Vol. abs/1602.06291, (2016) [Nogueira 19] Nogueira, R., Yang, W., Lin, J., and Cho, K.: Docu- ment Expansion by Query Prediction, CoRR, Vol. abs/1904.08375, (2019) [Rinott 15] Rinott, R., Dankin, L., Perez, C. A., Khapra, M. M., Aharoni, E., and Slonim, N.: Show Me Your Evidence - an Auto- matic Method for Context Dependent Evidence Detection, in Proc. EMNLP’15, pp. 440–450 (2015) [中辻 19] 中辻, 真, 奥井 颯平, 藤田 明久:LSTM を用いた Non-Factoid 型長文回答構築手法, 電子情報通信学会論文誌, Vol. J102- D, No. 4, pp. 267–276 (2019) [Liu 18] Liu, X., Duh, K., and Gao, J.: Stochastic Answer Networks for Natural Language Inference, CoRR, Vol. abs/1804.07888, (2018) [Yu 18] Yu, A. W., Dohan, D., Luong, M.-T., Zhao, R., Chen, K., Norouzi, M., and Le, Q. V.: QANet: Combining Local Convolu- tion with Global Self-Attention for Reading Comprehension, in Proc. ICLR’18 (2018) [Qiu 15] Qiu, X. and Huang, X.: Convolutional Neural Tensor Net- work Architecture for Community-based Question Answering, in Proc. IJCAI’15, pp. 1305–1311 (2015) [Wang 18] Wang, Y., Liu, K., Liu, J., He, W., Lyu, Y., Wu, H., Li, S., and Wang, H.: Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification, in Proc. ACL’18, pp. 1918–1927 (2018) [Li 16] Li, J., Monroe, W., Ritter, A., Jurafsky, D., Galley, M., and Gao, J.: Deep Reinforcement Learning for Dialogue Generation, in Proc. EMNLP’16, pp. 1192–1202 (2016) [Yang 16] Yang, Z., Yuan, Y., Wu, Y., Cohen, W. W., and Salakhutdi- nov, R.: Review Networks for Caption Generation, in Proc. NIPS’16, pp. 2361–2369 (2016) [Tan 17] Tan, C., Wei, F., Yang, N., Lv, W., and Zhou, M.: S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension, CoRR, Vol. abs/1706.04815, (2017) [Yin 16] Yin, J., Jiang, X., Lu, Z., Shang, L., Li, H., and Li, X.: Neural Generative Question Answering, in Proc. IJCAI’16, pp. 2972–2978 (2016) [Ennis 91] Ennis, R.: Critical Thinking: A Streamlined Conception, in Teaching Philosophy, pp. 5–25 (1991) [Tan 16] Tan, M., Santos, dos C. N., Xiang, B., and Zhou, B.: Im- proved Representation Learning for Question Answer Matching, in Proc. ACL’16, pp. 464–473 (2016) [Jia 17] Jia, R. and Liang, P.: Adversarial Examples for Evaluating Reading Comprehension Systems, in Proc. EMNLP’17, pp. 2021– 2031 (2017) [Yu 14] Yu, L., Hermann, K. M., Blunsom, P., and Pulman, S.: Deep Learning for Answer Sentence Selection, CoRR, Vol. abs/1412.1632, (2014) [Vinyals 15] Vinyals, O. and Le, Q. V.: A Neural Conversational Model, CoRR, Vol. abs/1506.05869, (2015) [Song 17] Song, H., Ren, Z., Liang, S., Li, P., Ma, J., and Ri- jke, de M.: Summarizing Answers in Non-Factoid Community Question-Answering, in Proc. WSDM ’17, pp. 405–414 (2017) [Papineni 02] Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J.: BLEU: A Method for Automatic Evaluation of Machine Translation, in Proc. ACL’02, pp. 311–318 (2002) [Santos15] Santos, dos C., Barbosa, L., Bogdanova, D., and Zadrozny, B.: Learning Hybrid Representations to Retrieve Seman- tically Equivalent Questions, in Proc. ACL-IJCNLP’15, pp. 694–699 (2015) [Joshi 17] Joshi, M., Choi, E., Weld, D. S., and Zettlemoyer, L.: Triv- iaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension, CoRR, Vol. abs/1705.03551, (2017) [Bahdanau 14] Bahdanau, D., Cho, K., and Bengio, Y.: Neural Ma- chine Translation by Jointly Learning to Align and Translate, CoRR, Vol. abs/1409.0473, (2014) [Nguyen 16] Nguyen, T., Rosenberg, M., Song, X., Gao, J., Ti-wary, S., Majumder, R., and Deng, L.: MS MARCO: A Human Gen- erated MAchine Reading COmprehension Dataset, in Proc. Work- shop on Cognitive Computation: Integrating Neural and Symbolic Approaches 2016 Co-located with NIPS 2016 (2016) [Nakatsuji 20b] Nakatsuji, M. and Okui, S.: Conclusion-Supplement Answer Generation for Non-Factoid Questions, in Proc. AAAI’20 (2020) [Wang 07] Wang, M., Smith, N. A., and Mitamura, T.: What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA, in Proc. EMNLP-CoNLL’07, pp. 22–32 (2007) [Yang 15] Yang, Y., Yih, W., and Meek, C.: WikiQA: A Challenge Dataset for Open-Domain Question Answering, in Proc. EMNLP’15, pp. 2013–2018 (2015) [Vaswani 17] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I.: Attention Is All You Need, in Proc. NIPS’17 (2017) [Lin 04] Lin, C.-Y.: ROUGE: A Package for Automatic Evaluation of Summaries, in Text Summarization Branches Out: In: Proc. ACL-04 Workshop, pp. 74–81 (2004) [Serban 16] Serban, I. V., Sordoni, A., Bengio, Y., Courville, A. C., and Pineau, J.: Building End-To-End Dialogue Systems Using Gen- erative Hierarchical Neural Network Models, in Proc. AAAI’16, pp. 3776–3784 (2016) [Sutskever 14] Sutskever, I., Vinyals, O., and Le, Q. V.: Sequence to Sequence Learning with Neural Networks, in Proc. NIPS’14, pp. 3104–3112 (2014) |
| References_xml | |
| SSID | ssib001234105 ssib008501343 ssib047348305 ssib000961560 ssib026596680 ssj0057238 ssib006575950 |
| Score | 2.3457935 |
| SourceID | jstage |
| SourceType | Publisher |
| StartPage | A-L64_1 |
| SubjectTerms | answer generation encoder-decoder model ensemble network non-factoid QA |
| Title | Non-Factoid型質問のための結論と理由で構成される回答文の生成手法 |
| URI | https://www.jstage.jst.go.jp/article/tjsai/37/2/37_37-2_A-L64/_article/-char/ja |
| Volume | 37 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| ispartofPNX | 人工知能学会論文誌, 2022/03/01, Vol.37(2), pp.A-L64_1-9 |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpR3LbtQwMCrlwoU34q0e8AmlJHHi2CfkbLOqeFRCaqVyipzHSt1Di2B74bY98NAi0UPbC1J7LAKBhCq-g4-IutvPYMZ5gnqgRVp5R2N7xjO2MzOJH4ZxD_EUXGPTT6gyYVA4plBJZtqZ8oRSwkp93I38dIHNL7mPlr3lqTO_WquW1gfxbPL62H0lp-lVwEG_4i7ZE_RsTRQQAEP_Qgo9DOk_9fHC2qrZxQtzgHPoERESHpCQk4ASKTXGI7xLQgr-IpFhCYgC4yDcZPkkgPIWVpcBEazK4pgFeM404JKgruWTkGGKTBnhXFcvWHgVi04FBLo9ATYS6Mg5JAW1gBH3_2gG4LGFNUEARMkC5CruyqzcaRJCeyT-gHgAZD1NoasBTjglwRxmATvJdOEOEbIlY90Ajhrj9StjXZZjPVRnSIIAx6ImDhS6TTFQsIfEkD9wY0UxhsqUoqbffrcCYXm9uEzPhpIX17oHoWVHa8hFvoXOJK2lryW-fzrZW0aIukxvLCtsdIPjVvkNq7RcxXE55Qx1WmZImk-YG9ktr0Ycay49Bz_YD_qv1Mos9U0n0hUb16BesFkO8UgXjagfOZjofxkhr6oA7hWM-hCwnHXgyY8m5_Gzlr8vmN2Ox8FtwmXGjQHA62Jbn9m5BxFKc16cwzyIzhuD4-LRTNpgFa6Xhzfo6TcspQLLdQkg5oO_hQSPsw_xV7V2U7uTixeN82UcOCMLgS4ZU3112bhQ3bEyU5rcK8bz1hw_3B0dHXw53P6YD7_nw718YwjA5Ofm0dedfPh5svlmsvUjH-6P90fjd5v5cDvf-JBvjA4_7U6-bY133mLhrT3IGr8fjQ-2rxpL3XCxM2-W16GYfZszeJDylNsQvli9mKae78eWSMCz7NlObPkqZllP2alFIf7nlDo9ZdlZJpgrYr8Xs9RX9Joxvbq2ml03ZhLhomVX1I1tV_BECYelwnfSxIoTi6U3jIeFcqIXxZk30UkHwM3_pnDLONdMydvG9ODlenYHAoBBfFcPqt-h3NiS |
| link.rule.ids | 315,783,787,27936,27937 |
| linkProvider | Colorado Alliance of Research Libraries |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Non-Factoid%E5%9E%8B%E8%B3%AA%E5%95%8F%E3%81%AE%E3%81%9F%E3%82%81%E3%81%AE%E7%B5%90%E8%AB%96%E3%81%A8%E7%90%86%E7%94%B1%E3%81%A7%E6%A7%8B%E6%88%90%E3%81%95%E3%82%8C%E3%82%8B%E5%9B%9E%E7%AD%94%E6%96%87%E3%81%AE%E7%94%9F%E6%88%90%E6%89%8B%E6%B3%95&rft.jtitle=%E4%BA%BA%E5%B7%A5%E7%9F%A5%E8%83%BD%E5%AD%A6%E4%BC%9A%E8%AB%96%E6%96%87%E8%AA%8C&rft.au=%E4%B8%AD%E8%BE%BB%2C+%E7%9C%9F&rft.au=%E5%85%AB%E5%B3%B6%2C+%E6%B5%A9%E6%96%87&rft.date=2022-03-01&rft.pub=%E4%B8%80%E8%88%AC%E7%A4%BE%E5%9B%A3%E6%B3%95%E4%BA%BA+%E4%BA%BA%E5%B7%A5%E7%9F%A5%E8%83%BD%E5%AD%A6%E4%BC%9A&rft.issn=1346-0714&rft.eissn=1346-8030&rft.volume=37&rft.issue=2&rft.spage=A-L64_1&rft.epage=9&rft_id=info:doi/10.1527%2Ftjsai.37-2_A-L64&rft.externalDocID=article_tjsai_37_2_37_37_2_A_L64_article_char_ja |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1346-0714&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1346-0714&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1346-0714&client=summon |