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024	8		_a10.1007/978-3-030-32
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037			_acom.springer.onix.9783030322335 _bSpringer Nature
050		4	_aQA76.9.N38 _bN572 2019eb
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049			_aMAIN
111	2		_aNLPCC (Conference) _n(8th : _d2019 : _cDunhuang, China) _968426
245	1	0	_aNatural language processing and Chinese computing : _b8th CCF International Conference, NLPCC 2019, Dunhuang, China, October 9-14, 2019, Proceedings. _nPart I / _cJie Tang, Min-Yen Kan, Dongyan Zhao, Sujian Li, Hongying Zan (eds.).
246	3		_aNLPCC 2019
264		1	_aCham, Switzerland : _bSpringer, _c2019.
300			_a1 online resource : _billustrations
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file
347			_bPDF
490	1		_aLecture Notes in Artificial Intelligence
490	1		_aLecture notes in computer science ; _v11838
490	1		_aLNCS sublibrary. SL 7, Artificial intelligence
500			_aInternational conference proceedings.
500			_aIncludes author index.
588	0		_aOnline resource; title from PDF title page (SpringerLink, viewed October 11, 2019).
505	0		_aIntro; Preface; Organization; Contents -- Part I; Contents -- Part II; Conversational Bot/QA/IR; Variational Attention for Commonsense Knowledge Aware Conversation Generation; 1 Introduction; 2 Related Work; 3 Model Description; 3.1 Task Definition; 3.2 Background: Knowledge Aware Framework; 3.3 Variational Attention for Knowledge Incorporation; 4 Experiments; 4.1 Data; 4.2 Settings; 4.3 Baselines; 4.4 Automatic Evaluation; 4.5 Manual Evaluation; 4.6 Extra Accurate Incorporation Evaluation; 4.7 Study Case; 5 Conclusion; References; Improving Question Answering by Commonsense-Based Pre-training
505	8		_a1 Introduction2 Tasks and Datasets; 3 Commonsense Knowledge; 4 Approach Overview; 5 Commonsense-Based Model; 6 Experiment; 6.1 Model Comparisons and Analysis; 6.2 Error Analysis and Discussion; 7 Related Work; 8 Conclusion; References; Multi-strategies Method for Cold-Start Stage Question Matching of rQA Task; 1 Introduction; 2 Related Work; 3 Methodology; 3.1 Densely-Connected Fusion Attentive Network; 3.2 Strategies; 4 Experiments; 4.1 Datasets; 4.2 Results of the DFAN Model; 4.3 Comparisons Between Strategies; 5 Analysis and Discussion; 5.1 Ablation Study on DFAN; 5.2 Effect of Data Size
505	8		_a6 Conclusion and Future WorkReferences; Multilingual Dialogue Generation with Shared-Private Memory; 1 Introduction; 2 Related Works; 2.1 Dialogue Systems; 2.2 Memory Networks; 2.3 Multi-task Learning; 3 Model; 3.1 Preliminary Background Knowledge; 3.2 Key-Value Memory Augmented Seq2Seq; 3.3 Seq2Seq with Shared-Private Memory; 4 Experimental Settings; 4.1 Datasets; 4.2 Evaluation Metrics; 4.3 Implementation Details; 4.4 Comparisons; 5 Results and Analysis; 5.1 Monolingual Models; 5.2 Multilingual Models; 5.3 Model Analysis; 6 Conclusion; References
505	8		_aLearning Personalized End-to-End Task-Oriented Dialogue Generation1 Introduction; 2 Related Work; 3 Method; 3.1 Problem Definition; 3.2 Framework Structure; 3.3 Training Objective; 4 Experimental Setup; 4.1 Experimental Data; 4.2 Model Configurations; 4.3 Baseline Methods; 4.4 Evaluation Metrics; 5 Experimental Results; 5.1 Ablation Study; 6 Conclusion and Future Work; References; SMART: A Stratified Machine Reading Test; 1 Introduction; 2 Related Work; 2.1 Related English Machine Comprehension Data Sets; 2.2 Related Chinese Machine Comprehension Data Sets; 3 Constructing the SMART Data Set
505	8		_a3.1 Source Data Preparation3.2 Stratified Question and Answer Design; 3.3 Key Statistics of the Data Set; 4 Establishing a Baseline; 5 Experiments; 6 Conclusion and Future Work; References; How Question Generation Can Help Question Answering over Knowledge Base; 1 Introduction; 2 Our Approach; 2.1 Dual Learning; 2.2 Fine Tuning; 3 Models; 3.1 QA Model; 3.2 QG Model; 4 Experiment; 4.1 Setup; 4.2 Relation Detection Results; 4.3 Comparison Results of Dual Learning; 4.4 QG Performance; 4.5 KBQA End-Task Results; 4.6 Case Study; 5 Related Work; 6 Conclusion; References
520			_aThis two-volume set of LNAI 11838 and LNAI 11839 constitutes the refereed proceedings of the 8th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2019, held in Dunhuang, China, in October 2019. The 85 full papers and 56 short papers presented were carefully reviewed and selected from 492 submissions. They are organized in the following topical sections: Conversational Bot/QA/IR; Knowledge graph/IE; Machine Learning for NLP; Machine Translation; NLP Applications; NLP for Social Network; NLP Fundamentals; Text Mining; Short Papers; Explainable AI Workshop; Student Workshop: Evaluation Workshop. -- _cProvided by publisher.
650		0	_aNatural language processing (Computer science) _vCongresses. _915085
650		0	_aChinese language _xData processing _vCongresses. _952629
650		6	_aTraitement automatique des langues naturelles _vCongrès. _916594
650		6	_aChinois (Langue) _xInformatique _vCongrès. _9967667
650		7	_aChinese language _xData processing _2fast _952630
650		7	_aNatural language processing (Computer science) _2fast
655		2	_aCongress _911670
655		7	_aproceedings (reports) _2aat
655		7	_aConference papers and proceedings _2fast _96065
655		7	_aConference papers and proceedings. _2lcgft _96065
655		7	_aActes de congrès. _2rvmgf _9609890
700	1		_aTang, Jie _c(Computer scientist), _eeditor. _1https://id.oclc.org/worldcat/entity/E39PCjKjTj9CXt3TwpPKQq3QpX
700	1		_aKan, Min-Yen, _eeditor. _968429
700	1		_aZhao, Dongyan, _eeditor. _968430
700	1		_aLi, Sujian, _eeditor. _964785
700	1		_aZan, Hongying, _eeditor. _964786
776	0		_z3030322327
830		0	_aLecture notes in computer science. _pLecture notes in artificial intelligence. _914916
830		0	_aLecture notes in computer science ; _v11838.
830		0	_aLNCS sublibrary. _nSL 7, _pArtificial intelligence. _920712
856	4	0	_uhttps://link.springer.com/10.1007/978-3-030-32233-5
938			_aAskews and Holts Library Services _bASKH _nAH36976439
938			_aProQuest Ebook Central _bEBLB _nEBL5924514
994			_a92 _bATIST
999			_c646788 _d646788