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

Comparing Rating Scales and Preference Judgements in Language Evaluation

Rating-scale evaluations are common in NLP, but are problematic for a range of reasons, e.g. they can be unintuitive for evaluators, inter-evaluator agreement and self-consistency tend to be low, and the parametric statistics commonly applied to the results are not generally considered appropriate for ordinal data. In this paper, we compare rating scales with an alternative evaluation paradigm, preferencestrength judgement experiments (PJEs), where evaluators have the simpler task of deciding which of two texts is better in terms of a given quality criterion. We present three pairs of evaluation experiments assessing text fluency and clarity for different data sets, where one of each pair of experiments is a rating-scale experiment, and the other is a PJE. We find the PJE versions of the experiments have better evaluator self-consistency and interevaluator agreement, and a larger proportion of variation accounted for by system differences, resulting in a larger number of significant differences being found.