Implicitness of Discourse Relations

Proceedings of COLING 2012: Technical Papers, pages 2669–2684, COLING 2012, Mumbai, December 2012. Implicitness of Discourse Relations Fatemeh Torabi Asr and V era Demberg Cluster of Excellence Multimodal Computing and Interaction (MMCI) Saarland University Campus C7.4, 66123 Saarbrücken, Germany fatemeh@coli.uni-saarland.de, vera@coli.uni-saarland.de Abstract The annotations of explicit and implicit discourse connectives in the Penn Discourse Treebank make it possible to investigate on a large scale how different types of discourse relations are expressed. Assuming an account of the Uniform Information Density hypothesis, we expect that discourse relations should be expressed explicitly with a discourse connector when they are unexpected, but may be implicit when the discourse relation can be anticipated. We investigate whether discourse relations which have been argued to be expected by the comprehender exhibit a higher ratio of implicit connectors. We find support for two hypotheses put forth in previous research which suggest that continuous and causal relations are presupposed by language users when processing consecutive sentences in a text. We then proceed to analyze the effect of Implicit Causality (IC) verbs (which have been argued to raise an expectation for an explanation) as a local cue for an upcoming causal relation. Keywords: Causality, Continuity, Discourse relations, Discourse cues, Implicit discourse relations, Corpus study, Uniform Information Density. 2669 1 Introduction David Hume, in his prominent work “An enquiry concerning human understanding” proposed that ideas in the human mind were associated according to at least three types of relations: resemblance, contiguity in time or place, and causality (Hume, 1784). Since then, many language scientists have tried to adapt this idea about human general reasoning to the world of language (Simon, 1952; Hobbs, 1990; Kehler, 2000). A discourse relation as defined by linguists is an inference intended by the writer or made by a reader to establish local coherence among individual sentences. For example, a binary causal relation refers to a logical cause-consequence inference whose elements are directly accessible in the text: two propositional arguments plus an operator that could be an explicit connective such as “because” (1-a). The operator might sometimes not be explicit in the text, but in most cases a suitable connective could be inserted to specify the coherence relation between the involved propositions (1-b). (1) a. Bill took his daughter to the hospital, because she looked pale and sick in the morning. b. I was very tired last night. [Therefore] I went to sleep earlier than usual. According to some cognitive theories on discourse processing, people have expectations about inter-sentential relations when reading a text, which bias their inferential decisions during comprehension (Segal et al., 1991; Murray, 1997; Levinson, 2000; Sanders, 2005). Two important characteristics that are expected to exist between consecutive events in a text are continuity and causality. Segal et al. (1991) and Murray (1997) argue that readers expect a sentence to be causally congruent and continuous with respect to its preceding context. Continuity in the sense of Segal et al. means that the same frame of reference is maintained, for example by subsequent sentences …

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