Abstract

Data-to-text Natural Language Generation (NLG) is the computational process of generating natural language in the form of text or voice from non-linguistic data. A core micro-planning task within NLG is referring expression generation (REG), which aims to automatically generate noun phrases to refer to entities mentioned as discourse unfolds. A limitation of novel REG models is not being able to generate referring expressions to entities not encountered during the training process. To solve this problem, we propose two extensions to NeuralREG, a state-of-the-art encoder-decoder REG model. The first is a copy mechanism, whereas the second consists of representing the gender and type of the referent as inputs to the model. Drawing on the results of automatic and human evaluation as well as an ablation study using the WebNLG corpus, we contend that our proposal contributes to the generation of more meaningful referring expressions to unseen entities than the original system and related work. Code and all produced data are publicly available.

Highlights

  • Data-to-text Natural Language Generation (NLG) is the computational process of generating natural language in the form of text or voice from non-linguistic data

  • Our proposed model outperformed the previous version of NeuralREG and presented competitive results compared to the current state-of-the-art in the literature

  • Despite its limitations in referring expression generation, OnlyNames baseline performed very well, which can be accounted for by the fact that WebNLG is a corpus made up of texts potentially used to yield encyclopedia entries, which allow for repetition of proper nouns unlike other types of text

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Summary

Introduction

Data-to-text Natural Language Generation (NLG) is the computational process of generating natural language in the form of text or voice from non-linguistic data. REG systems produce references to discourse entities in two explicit steps. They decide on the referential form, i.e., choosing whether a referring expression should be a pronoun (She), a proper name (Ada Lovelace), a description (The mathematician), etc. Once the choice is made, such systems textually realize the referring expression based on the chosen referential form and discourse context. If the first step selects a proper name as the form to refer to Ada Lovelace for instance, the ensuing step is responsible for deciding, among Ada, Ada Lovelace, or another text realization of a proper name, i.e., the one that is the most appropriate referring expression to that entity in a given discourse context

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