Towards Pragmatic Production Strategies for Natural Language Generation Tasks

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 7978 - 7984 December 7-11, 2022 ©2022 Association for Computational Linguistics Towards Pragmatic Production Strategies for Natural Language Generation Tasks Mario Giulianelli Institute for Logic, Language and Computation University of Amsterdam m.giulianelli@uva.nl Abstract This position paper proposes a conceptual framework for the design of Natural Language Generation (NLG) systems that follow efficient and effective production strategies in order to achieve complex communicative goals. In this general framework, efficiency is characterised as the parsimonious regulation of production and comprehension costs while effectiveness is measured with respect to task-oriented and con- textually grounded communicative goals. We provide concrete suggestions for the estimation of goals, costs, and utility via modern statisti- cal methods, demonstrating applications of our framework to the classic pragmatic task of visu- ally grounded referential games and to abstrac- tive text summarisation, two popular generation tasks with real-world applications. In sum, we advocate for the development of NLG systems that learn to make pragmatic production de- cisions from experience, by reasoning about goals, costs, and utility in a human-like way. 1 Introduction Novelists choose the right words to keep readers engaged and enthused, good journalists can con- vey facts clearly and convincingly, while poets may want to surprise the reader. Teachers adapt their explanations to the level of their students, and the language of parents changes with the pro- ficiency of their children, with the same objects described first using simplified funny expressions (‘moo moo’) and then more informative and dis- criminative names (‘cow’, ‘calf’). Using language to communicate successfully requires effort. On the side of the language producer, it is first of all ef- fortful to come up with words that truthfully corre- spond to one’s communicative intent. Then, words must be actually produced, e.g. said out loud or typed on a keyboard. At the same time, the pro- ducer has to take into consideration whether the comprehender—for whom, too, linguistic commu- nication is costly—will be able to infer the original intent. Comprehenders make efforts to pay atten- tion to the utterance they are being addressed with, to interpret it, and to infer their interlocutor’s com- municative intent. Fortunately, these efforts are often not in vain. They allow people to exchange knowledge, ideas, plans, and to achieve goals. This paper introduces a conceptual framework for Natural Language Generation (NLG) in vari- ably complex communicative scenarios, which re- lies on three main notions: communicative goals, production and comprehension costs, and utility. We define these notions formally and then, in two case studies, we provide suggestions for their op- erationalisation in classic NLG tasks. In sum, we model humans as decision makers striving for effi- cient and effective communication, and argue that human-like linguistic behaviour emerges as a result of reasoning about goals, costs, and utility. Learn- ing to navigate the complex decision space defined by these notions is still an open problem: we dis- cuss possible promising directions. 2 Doing Things with Words Communication always comes with …

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