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        <title>A Joint Sequential and Relational Model for Frame-Semantic Parsing</title>
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        <namePart type="given">Bishan</namePart>
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            <namePart type="given">Martha</namePart>
            <namePart type="family">Palmer</namePart>
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            <namePart type="given">Rebecca</namePart>
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    <abstract>We introduce a new method for frame-semantic parsing that significantly improves the prior state of the art. Our model leverages the advantages of a deep bidirectional LSTM network which predicts semantic role labels word by word and a relational network which predicts semantic roles for individual text expressions in relation to a predicate. The two networks are integrated into a single model via knowledge distillation, and a unified graphical model is employed to jointly decode frames and semantic roles during inference. Experiments on the standard FrameNet data show that our model significantly outperforms existing neural and non-neural approaches, achieving a 5.7 F1 gain over the current state of the art, for full frame structure extraction.</abstract>
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    <identifier type="doi">10.18653/v1/D17-1128</identifier>
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%0 Conference Proceedings
%T A Joint Sequential and Relational Model for Frame-Semantic Parsing
%A Yang, Bishan
%A Mitchell, Tom
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F yang-mitchell-2017-joint
%X We introduce a new method for frame-semantic parsing that significantly improves the prior state of the art. Our model leverages the advantages of a deep bidirectional LSTM network which predicts semantic role labels word by word and a relational network which predicts semantic roles for individual text expressions in relation to a predicate. The two networks are integrated into a single model via knowledge distillation, and a unified graphical model is employed to jointly decode frames and semantic roles during inference. Experiments on the standard FrameNet data show that our model significantly outperforms existing neural and non-neural approaches, achieving a 5.7 F1 gain over the current state of the art, for full frame structure extraction.
%R 10.18653/v1/D17-1128
%U https://aclanthology.org/D17-1128
%U https://doi.org/10.18653/v1/D17-1128
%P 1247-1256

Markdown (Informal)

[A Joint Sequential and Relational Model for Frame-Semantic Parsing](https://aclanthology.org/D17-1128) (Yang & Mitchell, EMNLP 2017)

• A Joint Sequential and Relational Model for Frame-Semantic Parsing (Yang & Mitchell, EMNLP 2017)

ACL

• Bishan Yang and Tom Mitchell. 2017. A Joint Sequential and Relational Model for Frame-Semantic Parsing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1247–1256, Copenhagen, Denmark. Association for Computational Linguistics.