There is a prevailing assumption in the literature on referring expression generation that relations are used in descriptions only 'as a last resort', typically on the basis that including the second entity in the relation introduces an additional cognitive load for either speaker or hearer. In this paper, we describe an experiemt that attempts to test this assumption; we determine that, even in simple scenes where the use of relations is not strictly required in order to identify an entity, relations are in fact often used. We draw some conclusions as to what this means for the development of algorithms for the generation of referring expressions.
The natural language generation literature provides many algorithms for the generation of referring expressions. In this paper, we explore the question of whether these algorithms actually produce the kinds of expressions that people produce. We compare the output of three existing algorithms against a data set consisting of human-generated referring expressions, and identify a number of significant differences between what people do and what these algorithms do. On the basis of these observations, we suggest some ways forward that attempt to address these differences.
In this paper, we explore a corpus of human-produced referring expressions to see to what extent we can learn the referential behaviour the corpus represents. Despite a wide variation in the way subjects refer across a set of ten stimuli, we demonstrate that component elements of the referring expression generation process appear to generalise across participants to a significant degree. This leads us to propose an alternative way of thinking of referring expression generation, where each attribute in a description is provided by a separate heuristic.
Abstract. Until recently, referring expression generation (reg) research focused on the task of selecting the semantic content of definite mentions of listener-familiar discourse entities. In the grec research programme we have been interested in a version of the reg problem definition that is (i) grounded within discourse context, (ii) embedded within an application context, and (iii) informed by naturally occurring data. This paper provides an overview of our aims and motivations in this research programme, the data resources we have built, and the first three sharedtask challenges, grec-msr'08, grec-msr'09 and grec-neg'09, we have run based on the data.
Automated writing assistance – a category that encompasses a variety of computer-based tools that help with writing – has been around in one form or another for 60 years, although it’s always been a relatively minor part of the NLP landscape. But the category has been given a substantial boost from recent advances in deep learning. We review some history, look at where things stand today, and consider where things might be going.
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