Generating referring expressions in context: the GREC task evaluation challenges, Empirical methods in natural language generation: data-oriented methods and empirical evaluation (original) (raw)

Managing Lexical Ambiguity in the Generation of Referring Expressions

International Journal of Intelligent Systems and Applications, 2013

Most existing algorithms for the Generation of Referring Expressions (GRE) tend to produce distinguishing descriptions at the semantic level, disregarding the ways in which surface issues (e.g. linguistic ambiguity) can affect their quality. In this article, we highlight limitations in an existing GRE algorithm that takes lexical ambiguity into account, and put forward some ideas to address those limitations. The proposed ideas are implemented in a GRE algorithm. We show that the revised algorithm successfully generates optimal referring expressions without greatly increasing the computational complexity of the (original) algorithm.

Towards the evaluation of referring expression generation

2006

The Natural Language Generation com- munity is currently engaged in discussion as to whether and how to introduce one or several shared evaluation tasks, as are found in other fields of Natural Language Processing. As one of the most well- defined subtasks in NLG, the generation of referring expressions looks like a strong candidate for piloting such shared tasks. Based on our earlier evaluation of a num- ber of existing algorithms for the genera- tion of referring expressions, we explore in this paper some problems that arise in designing an evaluation task in this field, and try to identify general considerations that need to be met in evaluating genera- tion subtasks.

Generation of referring expressions

Proceedings of the 22nd International Conference on Computational Linguistics - COLING '08, 2008

Existing algorithms for the Generation of Referring Expressions tend to generate distinguishing descriptions at the semantic level, disregarding the ways in which surface issues can affect their quality. This paper considers how these algorithms should deal with surface ambiguity, focussing on structural ambiguity. We propose that not all ambiguity is worth avoiding, and suggest some ways forward that attempt to avoid unwanted interpretations. We sketch the design of an algorithm motivated by our experimental findings.

Generation of Referring Expressions: Managing Structural Ambiguities

2008

Existing algorithms for the Generation of Referring Expressions tend to generate distinguishing descriptions at the semantic level, disregarding the ways in which surface issues can affect their quality. This paper considers how these algorithms should deal with surface ambiguity, focussing on structural ambiguity. We propose that not all ambiguity is worth avoiding, and suggest some ways forward that attempt to avoid unwanted interpretations. We sketch the design of an algorithm motivated by our experimental findings.

Referring Expression Generation: Taking Speakers’ Preferences into Account

Lecture Notes in Computer Science, 2014

We describe a classification-based approach to referring expression generation (REG) making use of standard context-related features, and an extension that adds speaker-related features. Results show that taking speakers' preferences into account outperforms the standard REG model in four test corpora of definite descriptions.

Content determination in the generation of referring expressions

Computational Intelligence, 1991

In this paper, we describe a general framework for the generation of referring expressions. Given an intended referent in the domain, domain-dependent mapping rules build a domain-independent level of recoverable semantic structure, which encodes the semantic content to be recovered by the hearer; a second set of mapping rules then builds an abstract syntactic structure, which is unified with a grammar and lexicon to produce a surface noun phrase. Within this framework, (i) we present a detailed description of the algorithms required to build the recoverable semantic structure, and discuss how the problem of infinite recursion can be handled when using relations in descriptions; and (ii) we show how the two levels of representation facilitate the generation of one-anaphoric noun phrases.