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            <namePart type="given">Mary</namePart>
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    <abstract>One of the most natural ways for human robot communication is through spoken language. Training human-robot interaction systems require access to large datasets which are expensive to obtain and labour intensive. In this paper, we describe an approach for learning from minimal data, using as a toy example language understanding in spoken dialogue systems. Understanding of spoken language is crucial because it has implications for natural language generation, i.e. correctly understanding a user’s utterance will lead to choosing the right response/action. Finally, we discuss implications for Natural Language Generation in Human-Robot Interaction.</abstract>
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%0 Conference Proceedings
%T Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction
%A Belakova, Jekaterina
%A Gkatzia, Dimitra
%Y Foster, Mary Ellen
%Y Buschmeier, Hendrik
%Y Gkatzia, Dimitra
%S Proceedings of the Workshop on NLG for Human–Robot Interaction
%D 2018
%8 November
%I Association for Computational Linguistics
%C Tilburg, The Netherlands
%F belakova-gkatzia-2018-learning
%X One of the most natural ways for human robot communication is through spoken language. Training human-robot interaction systems require access to large datasets which are expensive to obtain and labour intensive. In this paper, we describe an approach for learning from minimal data, using as a toy example language understanding in spoken dialogue systems. Understanding of spoken language is crucial because it has implications for natural language generation, i.e. correctly understanding a user’s utterance will lead to choosing the right response/action. Finally, we discuss implications for Natural Language Generation in Human-Robot Interaction.
%R 10.18653/v1/W18-6902
%U https://aclanthology.org/W18-6902/
%U https://doi.org/10.18653/v1/W18-6902
%P 8-11

Markdown (Informal)

[Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction](https://aclanthology.org/W18-6902/) (Belakova & Gkatzia, INLG 2018)

• Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction (Belakova & Gkatzia, INLG 2018)

ACL

• Jekaterina Belakova and Dimitra Gkatzia. 2018. Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction. In Proceedings of the Workshop on NLG for Human–Robot Interaction, pages 8–11, Tilburg, The Netherlands. Association for Computational Linguistics.