Atlas.txt : Linking Geo-referenced Data to Text for NLG

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Acquiring and Using Limited User Models in NLG

It is a truism of NLG that good knowledge of the reader can improve the quality of generated texts, and many NLG systems have been developed that exploit detailed user models when generating texts. Unfortunately, it is very difficult in practice to obtain detailed information about users. In this paper we describe our experiences in acquiring and using limited user models for NLG in four different systems, each of which took a different approach to this issue. One general conclusion is that it is useful if imperfect user models are understandable to users or domain experts, and indeed perhaps can be directly edited by them; this agrees with recent thinking about user models in other applications such as intelligent tutoring systems (Kay, 2001)

Data quality in reasoning

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Comprehension Driven Document Planning in Natural Language Generation Systems

This work is funded by the Engineering and Physical Sciences Research Council (EPSRC), under a National Productivity Investment Fund Doctoral Studentship (EP/R512412/1).Publisher PD

Error Analysis of ToTTo Table-to-Text Neural NLG Models

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Towards making NLG a voice for interpretable Machine Learning

I would like to acknowledge the support given to me by the Engineering and Physical Sciences Research Council (EPSRC) DTP grant number EP/N509814/1.Publisher PD

Studying the Impact of Filling Information Gaps on the Output Quality of Neural Data-to-Text

Acknowledgments We would like to thank our reviewers for their insightful feedback and questions. The work presented here is partially funded by the Engineering and Physical Sciences Research Council (EPSRC), which funds Craig Thomson under a National Productivity Investment Fund Doctoral Studentship (EP/R512412/1).Peer reviewedPublisher PD

Using spatial reference frames to generate grounded textual summaries of georeferenced data

Summarising georeferenced (can be identified according to it’s location) data in natural language is challenging because it requires linking events describing its nongeographic attributes to their underlying geography. This mapping is not straightforward as often the only explicit geographic information such data contains is latitude and longitude. In this paper we present an approach to generating textual summaries of georeferenced data based on spatial reference frames. This approach has been implemented in a data-to-text system we have deployed in the weather forecasting domain.

Are contrastive explanations useful?

Funding Information: Supported by EPSRC DTP Grant Number EP/N509814/1Peer reviewedPublisher PD

A simple surface realization engine for Telugu

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