Skip to main content
SpringerLink
Log in
Book cover

Developing Enterprise Chatbots pp 327–363Cite as

Rhetorical Agreement: Maintaining Cohesive Conversations

  • Chapter
  • First Online:

Abstract

To support a natural flow of a conversation in a chatbot, rhetorical structures of each message has to be analyzed. We classify a pair of paragraphs of text as appropriate for one to follow another, or inappropriate, based on communicative discourse considerations. To represent a multi-sentence message with respect to how it should follow a previous message in a conversation or dialogue, we build an extension of a discourse tree for it. Extended discourse tree is based on a discourse tree for RST relations with labels for communicative actions, and also additional arcs for anaphora and ontology-based relations for entities. We refer to such trees as Communicative Discourse Trees (CDTs). We explore syntactic and discourse features that are indicative of correct vs incorrect request-response or question-answer pairs. Two learning frameworks are used to recognize such correct pairs: deterministic, nearest-neighbor learning of CDTs as graphs, and a tree kernel learning of CDTs, where a feature space of all CDT sub-trees is subject to SVM learning. We form the positive training set from the correct pairs obtained from Yahoo Answers, social network, corporate conversations including Enron emails, customer complaints and interviews by journalists. The corresponding negative training set is artificially created by attaching responses for different, inappropriate requests that include relevant keywords. The evaluation showed that it is possible to recognize valid pairs in 70% of cases in the domains of weak request-response agreement and 80% of cases in the domains of strong agreement, which is essential to support automated conversations. These accuracies are comparable with the benchmark task of classification of discourse trees themselves as valid or invalid, and also with classification of multi-sentence answers in factoid question-answering systems. The applicability of proposed machinery to the problem of chatbots, social chats and programming via NL is demonstrated. We conclude that learning rhetorical structures in the form of CDTs is the key source of data to support answering complex questions, chatbots and dialogue management.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   89.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Airenti G, Bara BG, Colombetti M (1993) Conversation and behavior games in the pragmatics of dialogue. Cogn Sci 17:197–256

    Article  Google Scholar 

  • Allen J, Perrault C (1980) Analyzing intention in utterances. Artif Intell 15(3):143–178

    Article  Google Scholar 

  • Baumeister RF, Bushman BJ (2010) Social psychology and human nature: international edition. Wadsworth, Belmont

    Google Scholar 

  • Bengio Y, Ducharme R, Vincent P, Janvin C (2003) A neural probabilistic language model. J Mach Learn Res 3(March 2003):1137–1155

    MATH  Google Scholar 

  • Blaylock N, Allen J, Ferguson G (2003) Managing communicative intentions with collaborative problem solving. In: Current and new directions in discourse and dialogue. Springer Netherlands, Dordrecht, pp 63–84

    Chapter  Google Scholar 

  • Burstein JC, Braden-Harder L, Chodorow MS, Kaplan BA, Kukich K, Lu C, Rock DA, Wolff S (2002) System and method for computer-based automatic essay scoring. United States Patent 6,366,759: Educational Testing Service

    Google Scholar 

  • Cohen W (2016) Enron email dataset. https://www.cs.cmu.edu/~/enron/. Last downloaded 10 July 2016

  • Cohen PR, Levesque HJ (1990) Intention is choice with commitment. Artif Intell 42:213–261

    Article  MathSciNet  Google Scholar 

  • Collins M, Duffy N (2002) Convolution kernels for natural language. In: Proceedings of NIPS, pp 625–632

    Google Scholar 

  • Coulthard RM, Brazil D (1979) Exchange structure: discourse analysis monographs no. 5. The University of Birmingham, English Language Research, Birmingham

    Google Scholar 

  • CrimeRussia (2016) http://en.crimerussia.ru/corruption/shadow-chairman-of-the-investigative-committee

  • Cristea D, Ide N, Romary L (1998) Veins theory: a model of global discourse cohesion and coherence. In: Boitet C, Whitelock P (eds) 17th international conference on computational linguistics, vol 1. Association for Computational Linguistics, Montreal, pp 281–285

    Google Scholar 

  • De Boni M (2007) Using logical relevance for question answering. J Appl Log 5(1):92–103

    Article  MathSciNet  Google Scholar 

  • Dijkstra EW (1965) Programming considered as a human activity. In: Proceedings of the IFIP Congress, pp 213–217

    Google Scholar 

  • Galitsky B (2013) Machine learning of syntactic parse trees for search and classification of text. Eng Appl Artif Intell 26(3):1072–1091

    Article  Google Scholar 

  • Galitsky B (2016) Using extended tree kernels to recognize metalanguage in text. In: Kreinovich V (ed) Uncertainty modeling. Springer, Cham

    Google Scholar 

  • Galitsky B (2017) Matching parse thickets for open domain question answering. Data Knowl Eng 107:24–50

    Article  Google Scholar 

  • Galitsky B, Ilvovsky D (2017) On a chatbot finding answers with optimal rhetoric representation. In: Proceedings of recent advances in natural language processing, pp 253–259

    Google Scholar 

  • Galitsky B, Lebedeva N (2015) Recognizing documents versus meta-documents by tree kernel learning. In: FLAIRS conference, pp 540–545

    Google Scholar 

  • Galitsky B, McKenna EW (2017) Sentiment extraction from consumer reviews for providing product recommendations. US Patent 9,646,078

    Google Scholar 

  • Galitsky B, Shpitsberg I (2016) Autistic learning and cognition. In: Computational autism. Springer, Cham, pp 245–293

    Chapter  Google Scholar 

  • Galitsky B, Usikov D (2008) Programming spatial algorithms in natural language. AAAI workshop technical report WS-08-11, Palo Alto, pp 16–24

    Google Scholar 

  • Galitsky B, Kuznetsov SO, Samokhin MV (2005) Analyzing conflicts with concept-based learning. In: International conference on conceptual structures, pp 307–322

    Google Scholar 

  • Galitsky B, Dobrocsi G, de la Rosa JL, Kuznetsov SO (2011) Using generalization of syntactic parse trees for taxonomy capture on the web. In: International conference on conceptual structures, pp 104–117

    Google Scholar 

  • Galitsky B, Usikov D, Kuznetsov SO (2013) Parse thicket representations for answering multi-sentence questions. In: 20th international conference on conceptual structures. ICCS, p 95

    Google Scholar 

  • Galitsky B, Ilvovsky D, Lebedeva N, Usikov D (2014) Improving trust in automation of social promotion. In: AAAI Spring symposium on the intersection of robust intelligence and trust in autonomous systems, Stanford CA

    Google Scholar 

  • Galitsky B, Ilvovsky D, Kuznetsov SO (2015a) Text integrity assessment: sentiment profile vs rhetoric structure. CICLing-2015, Cairo

    Google Scholar 

  • Galitsky B, Ilvovsky D, Kuznetsov SO (2015b) Rhetoric map of an answer to compound queries. Knowledge Trail Inc. ACL 2015, Beijing, pp 681–686

    Google Scholar 

  • Galitsky B, Ilvovsky D, Kuznetsov SO (2015c) Text classification into abstract classes based on discourse structure. In: Proceedings of recent advances in natural language processing, Hissar, Bulgaria, 7–9 September 2015, pp 200–207

    Google Scholar 

  • Ganter B, Kuznetsov SO (2003) Hypotheses and Version Spaces, Proc. 10th Int. Conf. on Conceptual Structures, ICCS’03, Lecture Notes in Artificial Intelligence, vol 2746, pp 83–95

    Google Scholar 

  • Grefenstette E, Dinu G, Zhang Y, Sadrzadeh M and Baroni M (2013) Multi-step regression learning for compositional distributional semantics. In Proceedings of the Tenth International Conference on Computational Semantics. Association for Computational Linguistics

    Google Scholar 

  • Grosz B, Sidner C (1986) Attention, intention, and the structure of discourse. Comput Linguist 12(3):175–204

    Google Scholar 

  • Jansen P, Surdeanu M, Clark P (2014) Discourse complements lexical semantics for nonfactoid answer reranking. In: Proceedings of the 52nd ACL

    Google Scholar 

  • Joty SR, Carenini G, Ng RT (2016) CODRA: a novel discriminative framework for rhetorical analysis. Comput Linguist 41(3):385–435

    Article  MathSciNet  Google Scholar 

  • Jurafsky D, Martin JH (2000) Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Kate R, Wong YW, Mooney R (2005) Learning to transform natural to formal languages. Proc Natl Conf Artif Intell 20:1062–1068

    Google Scholar 

  • Kipper K, Korhonen A, Ryant N, Palmer M (2008) A large-scale classification of English verbs. Language Resources and Evaluation Journal 42:21–40

    Article  Google Scholar 

  • Kontos J, Malagardi I, Peros J (2016) Question answering and rhetoric analysis of biomedical texts in the AROMA system. Unpublished manuscript. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.379.5382. Last downloaded 12 September 2016

  • Kuznetsov SO (1999) Learning of simple conceptual graphs from positive and negative examples. In: European conference on principles of data mining and knowledge discovery. Springer, Berlin/Heidelberg, pp 384–391

    Chapter  Google Scholar 

  • Levinson SC (2000) Presumptive meanings: the theory of generalized conversational implicature. The MIT Press, Cambridge, MA

    Book  Google Scholar 

  • Litman DL, Allen JF (1987) A plan recognition model for subdialogues in conversation. Cogn Sci 11:163–200

    Article  Google Scholar 

  • Mann W, Thompson S (1988) Rhetorical structure theory: towards a functional theory of text organization. Text-Interdiscipl J Stud Discourse 8(3):243–281

    Article  Google Scholar 

  • Mikolov T, Chen K, Corrado GS, Jeffrey D (2015) Computing numeric representations of words in a high-dimensional space. US Patent 9,037,464, Google, Inc.

    Google Scholar 

  • Mitchell J, Lapata M (2010) Composition in distributional models of semantics. Cogn Sci 34(8):1388–1429

    Article  Google Scholar 

  • Mitocariu E, Anechitei DA, Cristea D (2016) Comparing discourse tree structures. Available from: https://www.researchgate.net/publication/262331642_Comparing_Discourse_Tree_Structures. Accessed 15 May 2016

  • Moschitti A, Quarteroni S, Basili R, and Manandhar S (2007) Exploiting syntactic and shallow semantic kernels for question/answer classification. In ACL’07, Prague, Czech Republic

    Google Scholar 

  • Peldszus A, Stede M (2013) From argument diagrams to argumentation mining in texts: a survey. Int J Cognit Informat Nat Intell 7(1):1–31

    Article  Google Scholar 

  • Popescu V, Caelen J, Burileanu C (2007) Logic-based rhetorical structuring for natural language generation in human-computer dialogue. Lect Notes Comput Sci 4629:309–317

    Article  Google Scholar 

  • Popescu-Belis A (2005) Dialogue acts: one or more dimensions? Tech report ISSCO working paper n. 62

    Google Scholar 

  • Radev DR, Jing H, Budzikowska M (2000) Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies. In: Proceedings of the 2000 NAACL-ANLP workshop on automatic summarization, vol 4

    Google Scholar 

  • Reichman R (1985) Getting computers to talk like you and me: discourse context, focus and semantics (an ATN model). MIT Press, Cambridge, MA/London

    Google Scholar 

  • Santhosh S, Ali J (2012) Discourse based advancement on question answering system. J Soft Comput 1(2):1–12

    Google Scholar 

  • Schiffrin D (2005) Discourse. In: Dittmar N, Trudgill P (eds) Handbook of sociolinguistics. Mouton, de Gruyter

    Google Scholar 

  • Scholman M, Evers-Vermeul J, Sanders T (2016) Categories of coherence relations in discourse annotation. Dialogue Discourse 7(2):1–28

    Google Scholar 

  • Socher RC, Manning D, Ng AY (2010) Learning continuous phrase representations and syntactic parsing with recursive neural networks. In: Proceedings of the NIPS-2010 deep learning and unsupervised feature learning workshop

    Google Scholar 

  • Sparck Jones K (1995) Summarising: analytic framework, key component, experimental method. In: Endres-Niggemeyer B, Hobbs J, Sparck Jones K (eds) Summarising text for intelligent communication, Dagstuhl seminar report 79, 13.12–17.12.93 (9350). Dagstuhl, Wadern

    Google Scholar 

  • Sperber D, Wilson D (1986) Relevance: communication and cognition. Blackwell/Oxford/Harvard University Press, Cambridge

    Google Scholar 

  • Surdeanu M, Hicks T, Valenzuela-Escarcega MA (2015) Two practical rhetorical structure theory parsers. In: Proceedings of the conference of the North American chapter of the association for computational linguistics – human language technologies: software demonstrations (NAACL HLT)

    Google Scholar 

  • Traum DR, Hinkelman EA (1992) Conversation acts in task-oriented spoken dialogue. Comput Intell 8(3):575–599

    Article  Google Scholar 

  • Tsui AMB (1994) English conversation. Describing english language series. Oxford University Press, London

    Google Scholar 

  • Yessenalina A, Cardie C (2011) Compositional matrix-space models for sentiment analysis. In: EMNLP’11. Association for Computational Linguistics, Stroudsburg, pp 172–182

    Google Scholar 

  • Walker MA, Passonneau RJ, Boland JE (2001) Quantitative and qualitative evaluation of DARPA communicator spoken dialogue systems. In: Proceedings of the ACL, pp 515–522

    Google Scholar 

  • Wang W, Su J, Tan CL (2010) Kernel based discourse relation recognition with temporal ordering information. ACL

    Google Scholar 

  • Wilks YA (ed) (1999) Machine conversations. Kluwer, New York

    Google Scholar 

  • Zanzotto FM, Korkontzelos I, Fallucchi F, Manandhar S (2010) Estimating linear models for 2112 compositional distributional semantics. In Proceedings of the 23rd International Conference 2113 on Computational Linguistics (COLING)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Oracle (United States), San Jose, CA, USA

    Boris Galitsky

Authors
  1. Boris Galitsky
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Galitsky, B. (2019). Rhetorical Agreement: Maintaining Cohesive Conversations. In: Developing Enterprise Chatbots. Springer, Cham. https://doi.org/10.1007/978-3-030-04299-8_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-04299-8_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04298-1

  • Online ISBN: 978-3-030-04299-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation