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        <title>A Joint Model for Semantic Sequences: Frames, Entities, Sentiments</title>
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    <abstract>Understanding stories – sequences of events – is a crucial yet challenging natural language understanding task. These events typically carry multiple aspects of semantics including actions, entities and emotions. Not only does each individual aspect contribute to the meaning of the story, so does the interaction among these aspects. Building on this intuition, we propose to jointly model important aspects of semantic knowledge – frames, entities and sentiments – via a semantic language model. We achieve this by first representing these aspects’ semantic units at an appropriate level of abstraction and then using the resulting vector representations for each semantic aspect to learn a joint representation via a neural language model. We show that the joint semantic language model is of high quality and can generate better semantic sequences than models that operate on the word level. We further demonstrate that our joint model can be applied to story cloze test and shallow discourse parsing tasks with improved performance and that each semantic aspect contributes to the model.</abstract>
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%0 Conference Proceedings
%T A Joint Model for Semantic Sequences: Frames, Entities, Sentiments
%A Peng, Haoruo
%A Chaturvedi, Snigdha
%A Roth, Dan
%Y Levy, Roger
%Y Specia, Lucia
%S Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F peng-etal-2017-joint
%X Understanding stories – sequences of events – is a crucial yet challenging natural language understanding task. These events typically carry multiple aspects of semantics including actions, entities and emotions. Not only does each individual aspect contribute to the meaning of the story, so does the interaction among these aspects. Building on this intuition, we propose to jointly model important aspects of semantic knowledge – frames, entities and sentiments – via a semantic language model. We achieve this by first representing these aspects’ semantic units at an appropriate level of abstraction and then using the resulting vector representations for each semantic aspect to learn a joint representation via a neural language model. We show that the joint semantic language model is of high quality and can generate better semantic sequences than models that operate on the word level. We further demonstrate that our joint model can be applied to story cloze test and shallow discourse parsing tasks with improved performance and that each semantic aspect contributes to the model.
%R 10.18653/v1/K17-1019
%U https://aclanthology.org/K17-1019
%U https://doi.org/10.18653/v1/K17-1019
%P 173-183

Markdown (Informal)

[A Joint Model for Semantic Sequences: Frames, Entities, Sentiments](https://aclanthology.org/K17-1019) (Peng et al., CoNLL 2017)

• A Joint Model for Semantic Sequences: Frames, Entities, Sentiments (Peng et al., CoNLL 2017)

ACL

• Haoruo Peng, Snigdha Chaturvedi, and Dan Roth. 2017. A Joint Model for Semantic Sequences: Frames, Entities, Sentiments. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 173–183, Vancouver, Canada. Association for Computational Linguistics.