120 research outputs found
Creating Training Corpora for NLG Micro-Planning
International audienceIn this paper, we focus on how to create data-to-text corpora which can support the learning of wide-coverage micro-planners i.e., generation systems that handle lexicalisation, aggregation, surface re-alisation, sentence segmentation and referring expression generation. We start by reviewing common practice in designing training benchmarks for Natural Language Generation. We then present a novel framework for semi-automatically creating linguistically challenging NLG corpora from existing Knowledge Bases. We apply our framework to DBpedia data and compare the resulting dataset with (Wen et al., 2016)'s dataset. We show that while (Wen et al., 2016)'s dataset is more than twice larger than ours, it is less diverse both in terms of input and in terms of text. We thus propose our corpus generation framework as a novel method for creating challenging data sets from which NLG models can be learned which are capable of generating text from KB data
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
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.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
NeuralREG: An end-to-end approach to referring expression generation
Traditionally, Referring Expression Generation (REG) models first decide on
the form and then on the content of references to discourse entities in text,
typically relying on features such as salience and grammatical function. In
this paper, we present a new approach (NeuralREG), relying on deep neural
networks, which makes decisions about form and content in one go without
explicit feature extraction. Using a delexicalized version of the WebNLG
corpus, we show that the neural model substantially improves over two strong
baselines. Data and models are publicly available.Comment: Accepted for presentation at ACL 201
Enhancing sequence-to-sequence modeling for RDF triples to natural text
Establishes key guidelines on how, which and when Machine Translation (MT) techniques are worth applying to RDF-to-Text task. Not only do we apply and compare the most prominent MT architecture, the Transformer, but we also analyze state-of-the-art techniques such as Byte Pair Encoding or Back Translation to demonstrate an improvement in generalization. In addition, we empirically show how to tailor these techniques to enhance models relying on learned embeddings rather than using pretrained ones. Automatic metrics suggest that Back Translation can significantly improve model performance up to 7 BLEU points, hence, opening a window for surpassing state-of-the-art results with appropriate architectures.Peer ReviewedPostprint (published version
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