Thiago Castro Ferreira

Referring Expression Generation: Taking Speakers’ Preferences into Account

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.

Classification-Based Referring Expression Generation

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.

Towards more variation in text generation: Developing and evaluating variation models for choice of referential form

In this study, we introduce a nondeterministic method for referring expression generation. We describe two models that account for individual variation in the choice of referential form in automatically generated text: a Naive Bayes model and a Recurrent Neural Network. Both are evaluated using the VaREG corpus. Then we select the best performing model to generate referential forms in texts from the GREC-2.0 corpus and conduct an evaluation experiment in which humans judge the coherence and comprehensibility of the generated texts, comparing them both with the original references and those produced by a random baseline model.

Individual Variation in the Choice of Referential Form

This study aims to measure the variation between writers in their choices of referential form by collecting and analysing a new and publicly available corpus of referring expressions. The corpus is composed of referring expressions produced by different participants in identical situations. Results, measured in terms of normalized entropy, reveal substantial individual variation. We discuss the problems and prospects of this finding for automatic text generation applications.

Task demands and individual variation in referring expressions

Aiming to improve the human-likeness of natural language generation systems, this study investigates different sources of variation that might influence the production of referring expressions (REs), namely the effect of task demands and inter- intra- individual variation. We collected REs using a discrimination game and varied the instructions, telling speakers that they would get points for being fast, creative, clear, or no incentive would be mentioned. Our results show that task demands affected REs production (number of words, number of attributes), and we observe a considerable amount of variation among the length of REs produced by single speakers, as well as among the REs of different speakers referring to the same targets.

Generating natural language descriptions using speaker-dependent information{*}

This paper discusses the issue of human variation in natural language referring expression generation. We introduce a model of content selection that takes speaker-dependent information into account to produce descriptions that closely resemble those produced by each individual, as seen in a number of reference corpora. Results show that our speaker-dependent referring expression generation model outperforms alternatives that do not take human variation into account, or which do so less extensively, and suggest that the use of machine-learning methods may be an ideal approach to mimic complex referential behaviour.

Towards proper name generation: a corpus analysis

We introduce a corpus for the study of proper name generation. The corpus consists of proper name references to people in webpages, extracted from the Wikilinks corpus. In our analyses, we aim to identify the different ways, in terms of length and form, in which a proper names are produced throughout a text.

Zoom: a corpus of natural language descriptions of map locations

This paper describes an experiment to elicit referring expressions from human subjects for research in natural language generation and related fields, and preliminary results of a computational model for the generation of these expressions. Unlike existing resources of this kind, the resulting data set the Zoom corpus of natural language descriptions of map locations takes into account a domain that is significantly closer to real-world applications than what has been considered in previous work, and addresses more complex situations of reference, including contexts with different levels of detail, and instances of singular and plural reference produced by speakers of Spanish and Portuguese.