Modelling the Usage of Discourse Connectives as Rational Speech Acts

Discourse relations can either be implicit or explicitly expressed by markers, such as ’therefore’ and ’but’. How a speaker makes this choice is a question that is not well understood. We propose a psycholinguistic model that predicts whether a speaker will produce an explicit marker given the discourse relation s/he wishes to express. Based on the framework of the Rational Speech Acts model, we quantify the utility of producing a marker based on the information-theoretic measure of surprisal, the cost of production, and a bias to maintain uniform information density throughout the utterance. Experiments based on the Penn Discourse Treebank show that our approach outperforms stateof-the-art approaches, while giving an explanatory account of the speaker’s choice.

[1]  Jet Hoek,et al.  The Role of Expectedness in the Implicitation and Explicitation of Discourse Relations , 2015, DiscoMT@EMNLP.

[2]  Nianwen Xue,et al.  Discovering Implicit Discourse Relations Through Brown Cluster Pair Representation and Coreference Patterns , 2014, EACL.

[3]  Bonnie L. Webber,et al.  Genre distinctions for discourse in the Penn TreeBank , 2009, ACL.

[4]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[5]  Marie-Catherine de Marneffe,et al.  The Overall Markedness of Discourse Relations , 2015, EMNLP.

[6]  Fatemeh Torabi Asr,et al.  Uniform Information Density at the Level of Discourse Relations: Negation Markers and Discourse Connective Omission , 2015 .

[7]  Linda Wheeldon,et al.  Syntactic priming in spoken sentence production – an online study , 2001, Cognition.

[8]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[9]  Rashmi Prasad,et al.  Reflections on the Penn Discourse TreeBank, Comparable Corpora, and Complementary Annotation , 2014, CL.

[10]  Austin F. Frank,et al.  Speaking Rationally: Uniform Information Density as an Optimal Strategy for Language Production , 2008 .

[11]  Gerhard Jäger,et al.  Game theory in semantics and pragmatics , 2012 .

[12]  G. Dell,et al.  Persistent structural priming from language comprehension to language production , 2007, Cognition.

[13]  Brendan T. O'Connor,et al.  Posterior calibration and exploratory analysis for natural language processing models , 2015, EMNLP.

[14]  J. K. Bock Syntactic persistence in language production , 1986, Cognitive Psychology.

[15]  Michael C. Frank,et al.  Predicting Pragmatic Reasoning in Language Games , 2012, Science.

[16]  Viktor Becher,et al.  When and why do translators add connectives?: A corpus-based study , 2011 .

[17]  S. Gries Syntactic Priming: A Corpus-based Approach , 2005, Journal of psycholinguistic research.

[18]  Alice Turk,et al.  The Smooth Signal Redundancy Hypothesis: A Functional Explanation for Relationships between Redundancy, Prosodic Prominence, and Duration in Spontaneous Speech , 2004, Language and speech.

[19]  Claudia Soria,et al.  Lexical marking of discourse relations - some experimental findings , 1998, COLING 1998.

[20]  Roger Levy,et al.  Speakers optimize information density through syntactic reduction , 2006, NIPS.

[21]  Vera Demberg,et al.  On the Information Conveyed by Discourse Markers , 2013, CMCL.

[22]  Sandrine Zufferey,et al.  Factors Influencing the Implicitation of Discourse Relations across Languages , 2015, ACL 2015.

[23]  Yuji Matsumoto,et al.  Modelling the Interpretation of Discourse Connectives by Bayesian Pragmatics , 2016, ACL.

[24]  Stephanie Kelter,et al.  Surface form and memory in question answering , 1982, Cognitive Psychology.

[25]  T. Jaeger,et al.  Implicit Learning and Syntactic Persistence: Surprisal and Cumulativity , 2007 .

[26]  S. Zufferey,et al.  A Multifactorial Analysis of Explicitation in Translation , 2014 .

[27]  Michael C. Frank,et al.  Embedded Implicatures as Pragmatic Inferences under Compositional Lexical Uncertainty , 2015, J. Semant..

[28]  Christopher Potts,et al.  Learning in the Rational Speech Acts Model , 2015, ArXiv.

[29]  Andrew Kehler,et al.  Predicting the Presence of Discourse Connectives , 2013, EMNLP.

[30]  Naomi Feldman,et al.  Why discourse affects speakers’ choice of referring expressions , 2015, ACL.

[31]  Ani Nenkova,et al.  Automatic sense prediction for implicit discourse relations in text , 2009, ACL.

[32]  Livio Robaldo,et al.  The Penn Discourse TreeBank 2.0. , 2008, LREC.

[33]  Siobhan Chapman Logic and Conversation , 2005 .

[34]  S. Levinson Presumptive Meanings: The theory of generalized conversational implicature , 2001 .

[35]  S. Piantadosi,et al.  Refer efficiently : Use less informative expressions for more predictable meanings , 2009 .

[36]  David Allbritton,et al.  Discourse Cues In Narrative Text: Using Production To Predict Comprehension , 1999 .

[37]  Michael C. Frank,et al.  Learning and using language via recursive pragmatic reasoning about other agents , 2013, NIPS.

[38]  Claire Cardie,et al.  Improving Implicit Discourse Relation Recognition Through Feature Set Optimization , 2012, SIGDIAL Conference.

[39]  Vera Demberg,et al.  Implicitness of Discourse Relations , 2012, COLING.

[40]  Ani Nenkova,et al.  Easily Identifiable Discourse Relations , 2008, COLING.

[41]  Eugene Charniak,et al.  Entropy Rate Constancy in Text , 2002, ACL.

[42]  Mihai Surdeanu,et al.  The Stanford CoreNLP Natural Language Processing Toolkit , 2014, ACL.

[43]  Roger Levy,et al.  Pragmatic reasoning through semantic inference , 2016, Semantics and Pragmatics.

[44]  Martin Paczynski,et al.  Establishing Causal Coherence across Sentences: An ERP Study , 2011, Journal of Cognitive Neuroscience.

[45]  T. Florian Jaeger,et al.  Redundancy and reduction: Speakers manage syntactic information density , 2010, Cognitive Psychology.

[46]  Alex Lascarides,et al.  Edinburgh Research Explorer Using automatically labelled examples to classify rhetorical relations: an assessment , 2022 .

[47]  Manfred Stede,et al.  Discourse Marker Choice in Sentence Planning , 1998, INLG.

[48]  Bonnie Webber,et al.  Implicitation of Discourse Connectives in (Machine) Translation , 2013, DiscoMT@ACL.

[49]  J. D. Murray Connectives and narrative text: The role of continuity , 1997, Memory & cognition.

[50]  E. M. Segal,et al.  The role of interclausal connectives in narrative structuring: Evidence from adults' interpretations of simple stories , 1991 .

[51]  Rashmi Prasad,et al.  Annotation of Discourse Relations for Conversational Spoken Dialogs , 2010, LREC.

[52]  Noah D. Goodman,et al.  Nonliteral understanding of number words , 2014, Proceedings of the National Academy of Sciences.

[53]  Noah D. Goodman,et al.  Knowledge and implicature: Modeling language understanding as social cognition , 2012, CogSci.

[54]  Clarisse Sieckenius de Souza,et al.  Getting the message across in RST-based text generation , 1990 .

[55]  Yuji Matsumoto,et al.  Crosslingual Annotation and Analysis of Implicit Discourse Connectives for Machine Translation , 2015, DiscoMT@EMNLP.

[56]  Hwee Tou Ng,et al.  Recognizing Implicit Discourse Relations in the Penn Discourse Treebank , 2009, EMNLP.

[57]  Johanna D. Moore,et al.  Investigating Cue Selection and Placement in Tutorial Discourse , 1995, ACL.