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Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

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arxiv 1703.09902 v4 pith:DJ7KNFER submitted 2017-03-29 cs.CL cs.AIcs.NE

Albert Gatt , Emiel Krahmer This is my paper · ORCID
classification cs.CLcs.AIcs.NE
keywords evaluationlanguagenaturalsurveytasksapplicationsareaschallenges
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This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.

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Cited by 4 Pith papers

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  1. MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance

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    MoverScore, which combines BERT embeddings with word mover distance, correlates with human judgments better than most existing unsupervised metrics across three of four text generation tasks.

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    A dual-component NLG system for culturally-attuned driving feedback in Nigeria is described, with a 90-driver pilot showing data collection but no behaviour-change results yet.

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    A survey of abstractive text summarization models, datasets, and metrics, accompanied by a small experimental run of public transformer checkpoints on short, long, and multi-document samples.

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