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Co-training for Implicit Discourse Relation Recognition Based on Manual and Distributed Features

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Abstract

Implicit discourse relation recognition aims to discover the semantic relation between two sentences where the discourse connective is absent. Due to the lack of labeled data, previous work tries to generate additional training data automatically by removing discourse connectives from explicit discourse relation instances. However, using these artificial data indiscriminately has been proven to degrade the performance of implicit discourse relation recognition. To address this problem, we propose a co-training approach based on manual features and distributed features, which identifies useful instances from these artificial data to enlarge the labeled data. In addition, the distributed features are learned via recursive autoencoder based approaches, capable of capturing to some extent the semantics of sentences which is valuable for implicit discourse relation recognition. Experiment results on both the PDTB and CDTB data sets indicate that: (1) The learned distributed features are complementary to the manual features, and thus suitable for co-training. (2) Our proposed co-training approach can use these artificial data effectively, and significantly outperforms the baselines.

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Notes

  1. In the PDTB, discourse relations are mainly defined between two clauses or sentences. Here, we use sentences for simplicity.

  2. For example, nonetheless is mapped into the Comparison relation.

  3. In this paper, we model implicit discourse relation recognition as four binary classification tasks (see Sect. 4.1).

  4. A number of classifiers can be used, including Maximum Entropy (ME) and so on. In our experiments, SVM achieves the best results.

  5. Randomly duplicate positive instances with replacement until the number of positive and negative instances are equal.

  6. The FBIS is a bilingual sentence aligned corpus, which consists of 237,870 Chinese-English sentence pairs with 6.72M Chinese words and 8.85M English words.

  7. We can also get artificial implicit instances from arbitrary text following the method in [5]. However, these artificial instances are much more noisy because it is hard to identify the positions of their arguments.

  8. We use all the selected artificial instances until the iteration \(K=200\) in Algorithm 1.

  9. The pdtb-parse toolkit also marks EntRel (entity-based coherence) instances as implicit discourse relation.

  10. https://github.com/percyliang/brown-cluster.

  11. In Chinese, explicit instances account for about 18.0%.

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Acknowledgements

We would like to thank all the reviewers for their constructive and helpful suggestions on this paper. This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61573294, 61303082, 61672440), the Ph.D. Programs Foundation of Ministry of Education of China (Grant No. 20130121110040), the Fund of Research Project of Tibet Autonomous Region of China (Grant No. Z2014A18G2-13), and the Natural Science Foundation of Fujian Province (Grant No. 2016J05161).

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Authors and Affiliations

  1. Fujian Key Lab of the Brain-like Intelligent Systems, Xiamen University, Xiamen, 361005, Fujian, China

    Changxing Wu, Xiaodong Shi, Yidong Chen & Yanzhou Huang

  2. Department of Cognitive Science, School of Information Science and Technology, Xiamen University, Xiamen, 361005, Fujian, China

    Changxing Wu, Xiaodong Shi, Yidong Chen & Yanzhou Huang

  3. School of Software, Xiamen University, Xiamen, 361005, Fujian, China

    Jinsong Su

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  1. Changxing Wu
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Correspondence to Xiaodong Shi.

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Wu, C., Shi, X., Su, J. et al. Co-training for Implicit Discourse Relation Recognition Based on Manual and Distributed Features. Neural Process Lett 46, 233–250 (2017). https://doi.org/10.1007/s11063-017-9582-x

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