Debbie, the Debate Bot of the Future

1. Abbott R, Ecker B, Anand P, Walker, M (2016) Internet argument corpus 2.0: an SQL schema for dialogic social media and the Corpora to go with it. LREC

   Google Scholar 

2. Ameixa D, Coheur L, Fialho P, Quaresma P (2014) Luke, i am your father: dealing with out-of-domain requests by using movies subtitles. In: International conference on intelligent virtual agents, Springer, Cham, pp 13–21

   Google Scholar 

3. Andrews P, De Boni M, Manandhar S, De M (2016) Persuasive argumentation in human computer dialogue. In AAAI spring symposium: argumentation for consumers of healthcare, pp 8–13

   Google Scholar 

4. Banchs RE, Li H (2012) IRIS: a chat-oriented dialogue system based on the vector space model. In: Proceedings of the ACL 2012 system demonstrations, pp 37–42. Association for Computational Linguistics

   Google Scholar 

5. Boltui F, Snajder J (2015) Identifying prominent arguments in online debates using semantic textual similarity. In: Proceedings of the 2nd workshop on argumentation mining, pp 110–115

   Google Scholar 

6. Bowden KK, Lin GI, Reed LI, Tree JEF, Walker MA (2016) M2d: monolog to dialog generation for conversational story telling. In: International conference on interactive digital storytelling. Springer, Cham, pp 12–24

   Google Scholar 

7. Conrad A, Wiebe J (2012) Recognizing arguing subjectivity and argument tags. In Proceedings of the workshop on extra-propositional aspects of meaning in computational linguistics, pp 80–88. Association for Computational Linguistics

   Google Scholar 

8. Duplessis GD, Letard V, Ligozat AL, Rosset S (2016) Purely corpus-based automatic conversation authoring. In: 10th edition of the language resources and evaluation conference (LREC)

   Google Scholar 

9. Elhadad Michael (1995) Using argumentation in text generation. J Pagmatics 24(1–2):189–220

   Article  Google Scholar 

10. Foner LN (1997) Entertaining agents: a sociological case study. In: Proceedings of the first international conference on autonomous agents. ACM, pp 122–129

    Google Scholar 

11. Goudas T, Louizos C, Petasis G, Karkaletsis V (2014) Argument extraction from news, blogs, and social media. In: Hellenic conference on artificial intelligence. Springer, Cham, pp 287–299

    Google Scholar 

12. Habernal I, Gurevych I (2015) Exploiting debate portals for semi-supervised argumentation mining in user-generated web discourse. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 2127–2137

    Google Scholar 

13. Habernal I, Gurevych I (2016) Which argument is more convincing? analyzing and predicting convincingness of web arguments using bidirectional lstm. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1: Long Papers) Vol 1, pp 1589–1599

    Google Scholar 

14. Han L, Kashyap A, Finin T, Mayfield J, Weese J (2013) UMBC_EBIQUITY-CORE: semantic textual similarity systems. second joint conference on lexical and computational semantics (* SEM), Volume 1: proceedings of the main conference and the shared task: semantic textual similarity, Vol 1

    Google Scholar 

15. Hasan KS, Ng V (2014) Why are you taking this stance? identifying and classifying reasons in ideological debates. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 751–762

    Google Scholar 

16. Higashinaka R, Imamura K, Meguro T, Miyazaki C, Kobayashi N, Sugiyama H, Matsuo Y (2014) Towards an open-domain conversational system fully based on natural language processing. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pp 928–939

    Google Scholar 

17. Liu C-W, Lowe R, Serban IV, Noseworthy M, Charlin L, Pineau J (2016) ZIB structure prediction pipeline: how NOT to evaluate your dialogue system: an empirical study of unsupervised evaluation metrics for dialogue response generation. arXiv preprint arXiv:1603.08023

18. Lukin S, Anand P, Walker M, Whittaker S, Argument strength is in the eye of the beholder: audience effects in persuasion

    Google Scholar 

19. Al-Khatib K, Wachsmuth H, Hagen M, Khler J, Stein B (2016) Cross-domain mining of argumentative text through distant supervision. In: Proceedings of the 2016 conference of the north american chapter of the association for computational linguistics: human language technologies, pp 1395–1404

    Google Scholar 

20. Mauldin ML (1994) Chatterbots, tinymuds, and the turing test: entering the loebner prize competition. AAAI 94:16–21

    Google Scholar 

21. Misra A, Anand P, Tree JF, Walker M (2015) Using summarization to discover argument facets in online idealogical dialog. NAACL HLT 2015

    Google Scholar 

22. Misra A, Ecker B, Walker MA (2016) Measuring the similarity of sentential arguments in dialog. In: 17th annual meeting of the special interest group on discourse and dialogue, p 276

    Google Scholar 

23. Misra A, Walker MA (2015) Topic independent identification of agreement and disagreement in social media dialogue. Evolution 460(460):920

    Google Scholar 

24. Nio L, Sakti S, Neubig G, Toda T, Adriani M, Nakamura S (2014) Developing non-goal dialog system based on examples of drama television. In: Natural interaction with robots, knowbots and smartphones, Springer, New York, NY, pp 355–361

    Google Scholar 

25. Oraby S, Reed L, Compton R, Riloff E, Walker M, Whittaker S, That AF (2015) Distinguishing factual and emotional argumentation in online dialogue. NAACL HLT 2015, 116

    Google Scholar 

26. Oraby S, Harrison V, Reed L, Hernandez E, Riloff E, Walker M (2016) Creating and characterizing a diverse corpus of sarcasm in dialogue. In: 17th annual meeting of the special interest group on discourse and dialogue, p 31

    Google Scholar 

27. Persing I, Ng V (2015) Modeling argument strength in student essays. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (Volume 1: Long Papers). Vol 1, pp 543–552

    Google Scholar 

28. Rinott R, Dankin L, Perez CA, Khapra MM, Aharoni E, Slonim N (2015) Show me your evidence-an automatic method for context dependent evidence detection. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 440–450

    Google Scholar 

29. Stab C, Kirschner C, Eckle-Kohler J, Gurevych I (2014) Argumentation mining in persuasive essays and scientific articles from the discourse structure perspective. In: ArgNLP, pp 21–25

    Google Scholar 

30. Swanson R, Ecker B, Walker M (2015) Argument mining: extracting arguments from online dialogue. In: Proceedings of the 16th annual meeting of the special interest group on discourse and dialogue, pp 217–226

    Google Scholar 

31. Walker MA, Anand P, Abbott R, Grant R (2012) Stance classification using dialogic properties of persuasion. In: Proceedings of the 2012 conference of the north american chapter of the association for computational linguistics: human language technologies, pp 592–596. Association for Computational Linguistics

    Google Scholar 

32. Walker MA, Anand P, Abbott R, Tree JEF, Martell C, King J (2012) That is your evidence?: classifying stance in online political debate. Decis Support Syst 53(4):719–729

    Article  Google Scholar 

33. Wang L, Ling W (2016) Neural network-based abstract generation for opinions and arguments. arXiv preprint arXiv:1606.02785

34. Weizenbaum J (2966) ELIZAa computer program for the study of natural language communication between man and machine. Commun ACM 9(1): 36–45

    Google Scholar 

35. Zukerman I, McConachy R, Korb, KB (2000) Using argumentation strategies in automated argument generation. Proceedings of the first international conference on Natural language generation, Vol 14. Association for Computational Linguistics, pp 55–62

    Google Scholar 

Download references