Algorithms for Generating Referring Expressions: Do They Do What People Do? (original) (raw)

Generation of referring expressions: Assessing the Incremental Algorithm

2012

Abstract A substantial amount of recent work in natural language generation has focused on the generation of ''one-shot''referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms.

Evaluating algorithms for the Generation of Referring Expressions using a balanced corpus

Despite being the focus of intensive research, evaluation of algorithms that generate referring expressions is still in its infancy. We describe a corpusbased evaluation methodology, applied to a number of classic algorithms in this area. The methodology focusses on balance and semantic transparency to enable comparison of human and algorithmic output. Although the Incremental Algorithm emerges as the best match, we found that its dependency on manually-set parameters makes its performance difficult to predict.

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.

Classification-Based Referring Expression Generation

Computational Linguistics and Intelligent Text Processing, 2014

This paper presents a study in the field of Natural Language Generation (NLG), focusing on the computational task of referring expression generation (REG). We describe a standard REG implementation based on the well-known Dale & Reiter Incremental algorithm, and a classification-based approach that combines the output of several support vector machines (SVMs) to generate definite descriptions from two publicly available corpora. Preliminary results suggest that the SVM approach generally outperforms incremental generation, which paves the way to further research on machine learning methods applied to the task.

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.

A Fast Algorithm for the Generation of Referring Expressions

1992

We simplify previous work in the development of algorithms for the generation of referring expre~ sions while at the same time taking account of psycholinguistic findings and transcript data. The result is a straightforward algorithm that is computationally tractable, sensitive to the preferences of human users, and reasonably domain-independent. We provide a specification of the resources a host system must provide in order to make use of the algorithm, and describe an implementation used in the IDAS system.