Abstract
Temporal coherence in discourse is provided through several temporal cohesive ties, such as tense, aspect and discourse connectives. In the relevance theoretic framework and more specifically in the Geneva school of pragmatics, these cohesive ties are considered as encoding procedural information important for guiding the hearer towards the intended interpretation of the discourse. Jacques Moeschler and his team studied temporal cohesive ties and proposed original theoretical models that have been validated with human and automatic annotation experiments, as well as in language acquisition studies (Zufferey and Popescu-Belis, this volume). In this paper, I show that Jacques Moeschler’s model for inferring temporal discourse relations and his description of tenses expressing past time in French is cross-linguistically valid and can be modelled for improving the results of statistical machine translation systems.
The ideas presented in this paper are based on a series of articles published with Bruno Cartoni, Thomas Meyer, Andrei Popescu-Belis, Michele Costagliola and Jacques Moeschler, with whom I collaborated on two research projects. I am very grateful to Jacques Moeschler for his guidance and resourceful discussions since the beginning of my research. A theoretical model of temporal reference in tensed languages based on empirical work (corpus-based and experiments with native speakers) is proposed in my Ph.D. dissertation (Grisot 2015), in which I make the proposal that temporal coherence in discourse is triggered by the hearer’s need to acquire temporal coherence at the cognitive level. In order to attain this purpose, he treats information coming from several sources (tense, grammatical aspect, lexical aspect, temporal adverbials and temporal connectives) and their rich interrelations in a coherent manner.
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Notes
- 1.
The COMTIS Project (Improving the Coherence of Machine Translation Output by Modeling Intersentential Relations; project n° CRSI22_127510, March 2010–July 2013) and the MODERN Project (Modeling discourse entities and relations for coherent machine translation; project n° CRSII2_147653, August 2013–August 2016) belong to the Sinergia interdisciplinary program funded by the Swiss National Science Foundation.
- 2.
At the beginning of the eighties, the label “Geneva School” was given to a series of publications on discourse and conversation that applied basic principles of syntactic analysis to the domain of discourse (Roulet et al. 1985; Moeschler 1985). In the beginning of the nineties, two different directions could be identified in the Geneva School: (i) a general discourse-oriented framework of language based on the modular hypothesis (Roulet 1997) and (ii) a radical pragmatic perspective on discourse sequencing and discourse interpretation (Moeschler 1993, 1996) (see detailed presentation in Moeschler 2001).
- 3.
- 4.
- 5.
I assume that this specialization conducted Kamp and Rohrer (1983) to argue that the PS encodes a forward temporal inference. I argue that the PS encodes a procedure regarding directional temporal inference. In other words, it is an instruction for the hearer to ascertain the contextual value of the directional temporal inference.
- 6.
- 7.
The narrativity feature is a coarse grained semantic and pragmatic feature proposed in a particular framework, that of Natural Language Processing and Machine Translation. I admit that from a theoretical point of view, only finer coarse features can explain all possible (both very frequent and less frequent) usages of a verb tense.
- 8.
Verb tense has frequently been associated with narrative contexts in various frameworks, such as in DRT and SDRT. Smith (2003) discussed discourse modes based in textual structure and aspect. If these theories focused on linguistic information and made use of non-monotonic inferences, for us narrativity is procedural information representing a cognitive (as opposed to logic in (S)DRT) discourse relation (Hobbs 1979; Mann and Thompson 1988; Sanders et al. 1992). Cognitive discourse relations are expressed lexically through verb tense and connectives (that are language-specific) and can occur in any type of stylistic register.
- 9.
At this point of the research, I consider lexical aspect as one class and do not distinguish between Vendler (1957). Moeschler (2000) discusses Dowty’s principle of interpretation of temporal discourses (1986) based on lexical aspect. Moeschler (2000) argues that this approach to temporal relations adopts a radical position and does not explain a certain number of exceptions.
- 10.
The translation of the SP through a present tense form can be explained by the contextual values taken by temporal coordinates S, R and E in order to lead to the speaker’s intended interpretation. Specifically, the translation with present time signals that the eventuality is viewed from the moment of speech (R = S) (see Grisot and Moeschler, submitted for publication, where we argue based on experimental results that temporal coordinates are conceptual information).
- 11.
Samardzic (2013) uses this novel methodology for investigating the translation equivalents of a range of English light verb constructions into several languages. Slavic languages encode verb aspect lexically, unlike other European languages. She applies the aspectual representation obtained in the English-Serbian cross-linguistic setting to classify English verbs into event duration classes.
- 12.
These results include only cases where inter-annotator agreement is high. Four participants judged each sentence and the result is based on the majority of answers.
- 13.
BLEU (Bilingual Evaluation Understudy) is an evaluation measure for machine-translated texts. It calculates the degree of resemblance to a human-translated text and it is a number between 0 and 1, where values closer to 1 represent more similar texts.
- 14.
Some of these suggestions are investigated in (Grisot 2015).
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Grisot, C. (2017). Temporal Coherence in Discourse: Theory and Application for Machine Translation. In: Blochowiak, J., Grisot, C., Durrleman, S., Laenzlinger, C. (eds) Formal Models in the Study of Language. Springer, Cham. https://doi.org/10.1007/978-3-319-48832-5_19
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