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        <namePart type="given">Peifeng</namePart>
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    <abstract>In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics. In this paper, we propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. In particular, besides encoding arguments (discourse units) using a gated convolutional network to obtain sentence-level representations, we train a simplified topic model to infer the latent topic-level representations. Moreover, we feed the two pairs of representations to two factored tensor networks, respectively, to capture both the sentence-level interactions and topic-level relevance using multi-slice tensors. Experimentation on CDTB, a Chinese discourse corpus, shows that our proposed model significantly outperforms several state-of-the-art baselines in both micro and macro F1-scores.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1058</identifier>
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%0 Conference Proceedings
%T Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese
%A Xu, Sheng
%A Li, Peifeng
%A Kong, Fang
%A Zhu, Qiaoming
%A Zhou, Guodong
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F xu-etal-2019-topic
%X In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics. In this paper, we propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. In particular, besides encoding arguments (discourse units) using a gated convolutional network to obtain sentence-level representations, we train a simplified topic model to infer the latent topic-level representations. Moreover, we feed the two pairs of representations to two factored tensor networks, respectively, to capture both the sentence-level interactions and topic-level relevance using multi-slice tensors. Experimentation on CDTB, a Chinese discourse corpus, shows that our proposed model significantly outperforms several state-of-the-art baselines in both micro and macro F1-scores.
%R 10.18653/v1/P19-1058
%U https://aclanthology.org/P19-1058
%U https://doi.org/10.18653/v1/P19-1058
%P 608-618

Markdown (Informal)

[Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese](https://aclanthology.org/P19-1058) (Xu et al., ACL 2019)

• Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese (Xu et al., ACL 2019)

ACL

• Sheng Xu, Peifeng Li, Fang Kong, Qiaoming Zhu, and Guodong Zhou. 2019. Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 608–618, Florence, Italy. Association for Computational Linguistics.