A Psycholinguistic Model for the Marking of Discourse Relations

Discourse relations can either be explicitly marked by discourse connectives (DCs), such as therefore and but , or implicitly conveyed in natural language utterances. How speakers choose between the two options is a question that is not well understood. In this study, we propose a psycholinguistic model that predicts whether or not speakers will produce an explicit marker given the discourse relation they wish to express. Our model is based on two information-theoretic frameworks: (1) the Rational Speech Acts model, which models the pragmatic interaction between language production and interpretation by Bayesian inference, and (2) the Uniform Information Density theory, which advocates that speakers adjust linguistic redundancy to maintain a uniform rate of information transmission. Specifically, our model quantifies the utility of using or omitting a DC based on the expected surprisal of comprehension, cost of production, and availability of other signals in the rest of the utterance. Experiments based on the Penn Discourse Treebank show that our approach outperforms the state-of-the-art performance at predicting the presence of DCs (Patterson and Kehler, 2013), in addition to giving an explanatory account of the speaker’s choice.

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

[2]  Merel Scholman,et al.  Categories of coherence relations in discourse annotation , 2016, Dialogue Discourse.

[3]  Man Lan,et al.  A Refined End-to-End Discourse Parser , 2015, CoNLL Shared Task.

[4]  Karl J. Friston,et al.  Predictive coding: an account of the mirror neuron system , 2007, Cognitive Processing.

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

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

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

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

[9]  Nancy Lockitch Loman,et al.  SIGNALING TECHNIQUES THAT INCREASE THE UNDERSTANDABILITY OF EXPOSITORY PROSE , 1983 .

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

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

[12]  Keith K. Millis,et al.  The Influence of Connectives on Sentence Comprehension , 1994 .

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

[14]  Henk Zeevat Bayesian interpretation and Optimality Theory , 2011 .

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

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

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

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

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

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

[21]  Leo G. M. Noordman,et al.  Toward a taxonomy of coherence relations , 1992 .

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

[23]  Leo Lentz,et al.  Coherence Marking, Prior Knowledge, and Comprehension of Informative and Persuasive Texts: Sorting Things Out , 2008 .

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

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

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

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

[28]  K. Haberlandt Reader Expectations in Text Comprehension , 1982 .

[29]  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.

[30]  Noah D. Goodman,et al.  Adjectival vagueness in a Bayesian model of interpretation , 2015, Synthese.

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

[32]  Brady Clark,et al.  Overspecification and the Cost of Pragmatic Reasoning about Referring Expressions , 2014, CogSci.

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

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

[35]  B. Meyer Use of Top-Level Structure in Text: Key for Reading Comprehension of Ninth-Grade Students. , 1980 .

[36]  Ted Sanders,et al.  The Role of Coherence Relations and Their Linguistic Markers in Text Processing , 2000 .

[37]  Noah D. Goodman,et al.  Context, scale structure, and statistics in the interpretation of positive-form adjectives , 2013 .

[38]  H. Rohde,et al.  Anticipatory looks reveal expectations about discourse relations , 2014, Cognition.

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

[40]  B. K. Britton,et al.  Effects of Text Structure on Use of Cognitive Capacity during Reading. , 1982 .

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

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

[43]  Kees van Deemter,et al.  Are we Bayesian referring expression generators , 2013 .

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

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

[46]  M. Pickering,et al.  Do people use language production to make predictions during comprehension? , 2007, Trends in Cognitive Sciences.

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

[48]  Annie Louis,et al.  Recovering discourse relations: Varying influence of discourse adverbials , 2015, LSDSem@EMNLP.

[49]  Kathleen McKeown,et al.  Discourse Planning with an N-gram Model of Relations , 2015, EMNLP.

[50]  Siobhan Chapman Logic and Conversation , 2005 .

[51]  Robert Dale,et al.  Computational Interpretations of the Gricean Maxims in the Generation of Referring Expressions , 1995, Cogn. Sci..

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

[53]  Matthew Stone,et al.  Anaphora and Discourse Structure , 2001, CL.

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

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

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

[57]  R. F. Lorch,et al.  On-Line Processing of Summary and Importance Signals in Reading. , 1986 .

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

[59]  付伶俐 打磨Using Language,倡导新理念 , 2014 .

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

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

[62]  Junyi Jessy Li,et al.  Assessing the Discourse Factors that Influence the Quality of Machine Translation , 2014, ACL.

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

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

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

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

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

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

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

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

[71]  Karl G. D. Bailey,et al.  Do speakers and listeners observe the Gricean Maxim of Quantity , 2006 .

[72]  Noah D. Goodman,et al.  Probabilistic Semantics and Pragmatics: Uncertainty in Language and Thought , 2015 .

[73]  Laurence Danlos Linguistic ways for expressing a discourse relation in a lexicalized text generation system , 1998, Workshop On Discourse Relations And Discourse Markers.

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

[75]  Henk Zeevat,et al.  Perspectives on Bayesian Natural Language Semantics and Pragmatics , 2015 .

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

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

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

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