Abstract Evaluation practices in natural language generation (NLG) have many known flaws,
but improved evaluation approaches are rarely widely adopted. This issue has become …

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 …

One of the main challenges in automatically generating textual weather forecasts is
choosing appropriate English words to communicate numeric weather data. A corpus-based …

Not everything is black and white. Our daily lives are full of vagueness or fuzziness.
Language is the most obvious example-for instance, when we describe someone as tall, it is …

We consider the evaluation problem in Natural Language Generation (NLG) and present
results for evaluating several NLG systems with similar functionality, including a knowledge …

There is growing interest in using automatically computed corpus-based evaluation metrics
to evaluate Natural Language Generation (NLG) systems, because these are often …

Two important recent trends in natural language generation are (i) probabilistic techniques
and (ii) comprehensive approaches that move away from traditional strictly modular and …

One of the biggest challenges in the development and deployment of spoken dialogue
systems is the design of the spoken language generation module. This challenge arises …

It is essential that data-to-text Natural Language Generation (NLG) systems produce texts
which are factually accurate. We examine accuracy issues in the task of generating …

Human evaluations are broadly thought to be more valuable the higher the inter-annotator
agreement. In this paper we examine this idea. We will describe our experiments and …