Influence of Situational Context on Language Production: Modelling Teachers’ Corrective Responses.

Abstract

Natural language is characterised by enormous linguistic variation (e.g., Fetzer (2003)). Such variation is not random, but is determined by a number of contextual factors. These factors encapsulate the socio-cultural conventions of a speech community and dictate the socially acceptable, i.e. polite, use of language. Producing polite language may not always be a trivial task. The ability to assess a situation with respect to a hearer’s social, cultural or emotional needs constitutes a crucial facet of a speaker’s social and linguistic competence. It is surprising then that it is also a facet which, to date, has received very little attention from researchers in the natural language generation community. Linguistic variation occurs in all linguistic sub-domains including the language of education (Person et al., 1995). Thanks to being relatively more constrained (and hence more predictable with respect to its intentional aspects than normal conversations), teachers’ language is taken in this thesis as a starting point for building a formal, computational model of language generation based on the theory of linguistic politeness. To date, the most formalised theory of linguistic politeness is that by Brown and Levinson (1987), in which face constitutes the central notion. With its two dimensions of Autonomy and Approval, face can be used to characterise different linguistic choices available to speakers in a systematic way. In this thesis, the basic idea of face is applied in the analysis of teachers’ corrective responses produced in real one-to-one and classroom dialogues, and it is redefined to suit the educational context. A computational model of selecting corrective responses is developed which demonstrates how the two dimensions of face can be derived from a situation and how they can be used to classify the many linguistic choices available to teachers. The model is fully implemented using a combination of naive Bayesian Networks and Case-Based Reasoning techniques. The evaluation of the model confirms the validity of the model, by demonstrating that politeness-based natural language generation in the context of teachers’ corrective responses can be used to model linguistic variation and that the resulting language is not singnificantly different from that produced by a human in identical situations.