SimpleNLG : a realisation engine for practical applications

This paper describes SimpleNLG, a realisation engine for English which aims to provide simple and robust interfaces to generate syntactic structures and linearise them. The library is also flexible in allowing the use of mixed (canned and noncanned) representations.peer-reviewe

If it may have happened before, it happened, but not necessarily before

Temporal uncertainty in raw data can impede the inference of temporal and causal relationships between events and compromise the output of data-to-text NLG systems. In this paper, we introduce a framework to reason with and represent temporal uncertainty from the raw data to the generated text, in order to provide a faithful picture to the user of a particular situation. The model is grounded in experimental data from multiple languages, shedding light on the generality of the approach.peer-reviewe

Textual properties and task based evaluation : investigating the role of surface properties, structure and content

This paper investigates the relationship between the results of an extrinsic, task-based evaluation of an NLG system and various metrics measuring both surface and deep semantic textual properties, including relevance. The latter rely heavily on domain knowledge. We show that they correlate systematically with some measures of performance. The core argument of this paper is that more domain knowledge-based metrics shed more light on the relationship between deep semantic properties of a text and task performance.peer-reviewe

Adversarial Generation of Natural Language

Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation. Advances in the adversarial generation of natural language from noise however are not commensurate with the progress made in generating images, and still lag far behind likelihood based methods. In this paper, we take a step towards generating natural language with a GAN objective alone. We introduce a simple baseline that addresses the discrete output space problem without relying on gradient estimators and show that it is able to achieve state-of-the-art results on a Chinese poem generation dataset. We present quantitative results on generating sentences from context-free and probabilistic context-free grammars, and qualitative language modeling results. A conditional version is also described that can generate sequences conditioned on sentence characteristics.Comment: 11 pages, 3 figures, 5 table

Building a semantically transparent corpus for the generation of referring expressions

This paper discusses the construction of a corpus for the evaluation of algorithms that generate referring expressions. It is argued that such an evaluation task requires a semantically transparent corpus, and controlled experiments are the best way to create such a resource. We address a number of issues that have arisen in an ongoing evaluation study, among which is the problem of judging the output of GRE algorithms against a human gold standard.peer-reviewe