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Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese

Summary

Implicit discourse relations, where two adjacent text units are linked without an explicit connective, are much more frequent in Chinese than in English. Chinese is paratactic and often omits connectives. Most existing models were built for English and rely only on sentence-level argument representations, which the authors argue carry too little information for Chinese.

They propose the Topic Tensor Network (TTN), which combines two views of each argument:

  • a sentence-level encoding from a GCN (Gated Convolutional Network);
  • a topic-level encoding from a Simplified Topic Model (STM), a VAE-style neural topic model that infers a latent topic distribution from the bag-of-words input.

Each pair of representations goes into its own Factored Tensor Network (FTN). The FTN uses multi-slice tensors with factorization to model interactions between the arguments at lower cost. An MLP classifier then predicts the relation.

On the Chinese corpus CDTB, TTN outperforms a number of leading prior models on micro F1 as well as macro F1. This shows that topic continuity is a useful signal for Chinese discourse parsing.

Key points

  • Implicit relations make up 75.2% of CDTB (500 newswire documents) but only about 40% of the English PDTB. The relation classes are Causality, Coordination, Elaboration and Transition.
  • Adding the FTNs raises F1 for Elaboration by 3.9 points and for Transition by 8.0 points.
  • Factorization cuts tensor parameters about 20-fold compared with the NTN of Guo et al. (2018). Substituting that NTN lowers micro F1 by 1.0 and macro F1 by 1.4.
  • Most errors involve the majority class Coordination, which is 61.6% of the training data. For example, 51.3% of Causality samples are mislabeled as Coordination.

Our summary of the paper, in our own words β€” not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Topics: Discourse analysis

Cited by

7 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 2 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/p19-1058

@inproceedings{xu2019topic,
  title = {{Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese}},
  author = {Xu, Sheng and Li, Peifeng and Kong, Fang and Zhu, Qiaoming and Zhou, Guodong},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.18653/v1/p19-1058},
  url = {https://doi.org/10.18653/v1/p19-1058}
}

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