3 research outputs found
Lessons from Deploying NLG Technology for Marine Weather Forecast Text Generation
Abstract. SUMTIME-MOUSAM is a Natural Language Generation (NLG) system that produces textual weather forecasts for offshore oilrigs from Numerical Weather Prediction (NWP) data. It has been used for the past year by Weathernews (UK) Ltd for producing 150 draft forecasts per day, which are then post-edited by forecasters before being released to end-users. In this paper, we describe how the system works, how it is used at Weathernews and finally some lessons we learnt from building, installing and maintaining SUMTIME-MOUSAM. One important lesson has been that using NLG technology improves maintainability although the biggest maintenance work actually involved changing data formats at the I/O interfaces. We also found our system being used by forecasters in unexpected ways for understanding and editing data. We conclude that the success of a technology owes as much to its functional superiority as to its suitability to the various stakeholders such as developers and users
Deep Learning for Text Style Transfer: A Survey
Text style transfer is an important task in natural language generation,
which aims to control certain attributes in the generated text, such as
politeness, emotion, humor, and many others. It has a long history in the field
of natural language processing, and recently has re-gained significant
attention thanks to the promising performance brought by deep neural models. In
this paper, we present a systematic survey of the research on neural text style
transfer, spanning over 100 representative articles since the first neural text
style transfer work in 2017. We discuss the task formulation, existing datasets
and subtasks, evaluation, as well as the rich methodologies in the presence of
parallel and non-parallel data. We also provide discussions on a variety of
important topics regarding the future development of this task. Our curated
paper list is at https://github.com/zhijing-jin/Text_Style_Transfer_SurveyComment: Computational Linguistics Journal 202