---
title: "A Regularization Approach for Incorporating Event — Dai 2019"
description: "Implicit discourse relations in PDTB, which lack an explicit connective, remain hard to classify."
authors: ["Zeyu Dai", "Ruihong Huang"]
year: 2019
venue: "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)"
source: "https://aclanthology.org/D19-1295/"
doi: "10.18653/v1/d19-1295"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/dai-2019-regularization-approach-incorporating-event-knowledge/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/dai-2019-regularization-approach-incorporating-event-knowledge/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2019](https://md.research.elamur.ai/free/en/papers/2019/) / A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing

# A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing

**Authors:** [Zeyu Dai](https://md.research.elamur.ai/free/en/authors/zeyu-dai/), [Ruihong Huang](https://md.research.elamur.ai/free/en/authors/ruihong-huang/) · **Year:** 2019 · **Venue:** Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D19-1295/) · [PDF](https://aclanthology.org/D19-1295.pdf) · [DOI 10.18653/v1/d19-1295](https://doi.org/10.18653/v1/d19-1295) · [OpenAlex](https://openalex.org/W2971329390)

**Citations:** 29 citations (openalex-snapshot · 2026-10-14) · [17](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Implicit discourse relations in PDTB, which lack an explicit connective, remain hard to classify. Earlier systems reached macro F1 below 50%, and the authors attribute much of this gap to data sparsity. Dai and Huang propose adding external event knowledge (for example, causal or temporal links between event words) and entity coreference links to a neural parser without letting these signals override context. They build on their paragraph-level hierarchical BiLSTM model with a CRF output layer. Between the word-level and DU-level BiLSTMs, they insert a knowledge layer that transforms the hidden states of words involved in a relation using relation-type-specific feedforward networks. A TransE-style regularization term added to the training loss constrains these transformed vectors, which balances attention between knowledge and context. On PDTB v2.0, the approach improves both implicit and explicit relation classification and outperforms previous systems. Removing the regularizer makes results worse than the base model. This indicates that external knowledge needs controlled integration to help rather than hurt.

## Key points
- The base model is a re-implementation of Dai and Huang (2018) with character-level CNN features. It supports 300D GloVe or 1024D ELMo embeddings. POS, NER and coreference annotations come from Stanford CoreNLP.
- With ELMo, the full model improves over the base model by 2.1 macro-F1 and 3.2 accuracy points on top-level implicit relations. It also beats Bai and Zhao (2018), the strongest earlier system, by more than 1.8 macro-F1.
- Several alternatives gained little over the base model: adding knowledge as discrete word-level or DU-level features, or using a two-step approach with fixed knowledge embeddings plus attention. The model also improves second-level fine-grained classification.
- On the dev set, the gains come mostly from the 507 implicit instances that carry event or coreference constraints. Errors on these instances fell from 217 to 193, with 60 corrections against 36 false reversions. On the 717 unconstrained instances, results were similar (407 vs. 402 correct).
- About one third of the reversions came from noisy event knowledge or coreference errors, and the rest from over-reliance on constraints. The authors suggest antonym knowledge as future work to help with Comparison relations.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) · [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/)

## Cited by

Cited by

17 papers of our library cite it (of the 140884 whose bibliographies we read); 11 of them have a page here, 6 not yet.

- [DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/chan-2023-discoprompt-path-prediction-prompt-tuning/)
- [Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations](https://md.research.elamur.ai/free/en/papers/chan-2024-exploring-potential-chatgpt-sentence-level/)
- [Coreference for Discourse Parsing: A Neural Approach](https://md.research.elamur.ai/free/en/papers/guz-2020-coreference-discourse-parsing-neural-approach/)
- [Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/jiang-2023-global-local-hierarchy-aware-contrastive/)
- [Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives](https://md.research.elamur.ai/free/en/papers/kishimoto-2020-adapting-bert-implicit-discourse-relation/)
- [Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations](https://md.research.elamur.ai/free/en/papers/long-2022-facilitating-contrastive-learning-discourse-relational/)
- [QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines](https://md.research.elamur.ai/free/en/papers/pyatkin-2020-qadiscourse-discourse-relations-as-qa/)
- [Connective Prediction for Implicit Discourse Relation Recognition via Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/wu-2023-connective-prediction-implicit-discourse-relation/)
- [Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/zhang-2021-context-tracking-network-graph-based/)
- [Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/zhao-2023-infusing-hierarchical-guidance-into-prompt/)
- [Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/zhou-2022-prompt-based-connective-prediction-method/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{dai2019regularization,
  title = {{A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing}},
  author = {Dai, Zeyu and Huang, Ruihong},
  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  year = {2019},
  doi = {10.18653/v1/d19-1295},
  url = {https://doi.org/10.18653/v1/d19-1295}
}
```

- [← previous A Probabilistic Approach to Syntactic Variation in Biblical Hebrew](https://md.research.elamur.ai/free/en/papers/hbo-2019-bijl-probabilistic-approach-syntactic-variation-biblical/)
- [next → A Simple Joint Model for Improved Contextual Neural Lemmatization](https://md.research.elamur.ai/free/en/papers/ud-2019-malaviya-simple-joint-model-improved-contextual/)
- [↑ back to papers of 2019](https://md.research.elamur.ai/free/en/papers/2019/)

## References

59 references, 30 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In ICLR. [find](https://scholar.google.com/scholar?q=Dzmitry+Bahdanau%2C+Kyunghyun+Cho%2C+and+Yoshua+Bengio%2E+2015%2E+Neural+machine+translation+by+jointly+learning+to+align+and+translate%2E+In+ICLR%2E) [our card](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/)
2. Hongxiao Bai and Hai Zhao. 2018. Deep enhanced representation for implicit discourse relation recognition. In COLING, pages 571–583. [find](https://scholar.google.com/scholar?q=Hongxiao+Bai+and+Hai+Zhao%2E+2018%2E+Deep+enhanced+representation+for+implicit+discourse+relation+recognition%2E+In+COLING%2C+pages+571%E2%80%93583%2E) [our card](https://md.research.elamur.ai/free/en/papers/bai-2018-deep-enhanced-representation-implicit-discourse/)
3. Or Biran and Kathleen McKeown. 2013. Aggregated word pair features for implicit discourse relation disambiguation. In ACL, volume 2, pages 69–73. [find](https://scholar.google.com/scholar?q=Or+Biran+and+Kathleen+McKeown%2E+2013%2E+Aggregated+word+pair+features+for+implicit+discourse+relation+disambiguation%2E+In+ACL%2C+volume+2%2C+pages+69%E2%80%9373%2E) [our card](https://md.research.elamur.ai/free/en/papers/biran-2013-aggregated-word-pair-features-implicit/)
4. Or Biran and Kathleen McKeown. 2015. Pdtb discourse parsing as a tagging task: The two taggers approach. In Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 96–104. [find](https://scholar.google.com/scholar?q=Or+Biran+and+Kathleen+McKeown%2E+2015%2E+Pdtb+discourse+parsing+as+a+tagging+task%3A+The+two+taggers+approach%2E+In+Proceedings+of+the+16th+Annual+Meeting+of+the+Special+Interest+Group+on+Discourse+and+Dialog) [our card](https://md.research.elamur.ai/free/en/papers/biran-2015-pdtb-discourse-parsing-as-tagging/)
5. Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In NeurIPS, pages 2787–2795. [find](https://scholar.google.com/scholar?q=Antoine+Bordes%2C+Nicolas+Usunier%2C+Alberto+Garcia%2DDuran%2C+Jason+Weston%2C+and+Oksana+Yakhnenko%2E+2013%2E+Translating+embeddings+for+modeling+multi%2Drelational+data%2E+In+NeurIPS%2C+pages+2787%E2%80%932795%2E)
6. Antoine Bosselut, Asli Celikyilmaz, Xiaodong He, Jianfeng Gao, Po-Sen Huang, and Yejin Choi. 2018. Discourse-aware neural rewards for coherent text generation. In NAACL-HLT, pages 173–184. [find](https://scholar.google.com/scholar?q=Antoine+Bosselut%2C+Asli+Celikyilmaz%2C+Xiaodong+He%2C+Jianfeng+Gao%2C+Po%2DSen+Huang%2C+and+Yejin+Choi%2E+2018%2E+Discourse%2Daware+neural+rewards+for+coherent+text+generation%2E+In+NAACL%2DHLT%2C+pages+173%E2%80%93184%2E)
7. Chloé Braud and Pascal Denis. 2015. Comparing word representations for implicit discourse relation classification. In EMNLP, pages 2201–2211. [find](https://scholar.google.com/scholar?q=Chloe%CC%81+Braud+and+Pascal+Denis%2E+2015%2E+Comparing+word+representations+for+implicit+discourse+relation+classification%2E+In+EMNLP%2C+pages+2201%E2%80%932211%2E) [our card](https://md.research.elamur.ai/free/en/papers/braud-2015-comparing-word-representations-implicit-discourse/)
8. Lynn Carlson, Daniel Marcu, and Mary Ellen Okurowski. 2003. Building a discourse-tagged corpus in the framework of rhetorical structure theory. In Current and new directions in discourse and dialogue, pages 85–112. Springer. [find](https://scholar.google.com/scholar?q=Lynn+Carlson%2C+Daniel+Marcu%2C+and+Mary+Ellen+Okurowski%2E+2003%2E+Building+a+discourse%2Dtagged+corpus+in+the+framework+of+rhetorical+structure+theory%2E+In+Current+and+new+directions+in+discourse+and+dialogue%2C) [our card](https://md.research.elamur.ai/free/en/papers/carlson-2001-building-discourse-tagged-corpus-framework/)
9. Bhuwan Dhingra, Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov. 2017. Linguistic knowledge as memory for recurrent neural networks. arXiv preprint arXiv:1703.02620. — [arXiv:1703.02620](https://arxiv.org/abs/1703.02620) [find](https://scholar.google.com/scholar?q=Bhuwan+Dhingra%2C+Zhilin+Yang%2C+William+W+Cohen%2C+and+Ruslan+Salakhutdinov%2E+2017%2E+Linguistic+knowledge+as+memory+for+recurrent+neural+networks%2E+arXiv+preprint+arXiv%3A1703%2E02620%2E+%E2%80%94+%5BarXiv%3A1703%2E02620%5D%28https%3A)
10. Shima Gerani, Yashar Mehdad, Giuseppe Carenini, Raymond T Ng, and Bita Nejat. 2014. Abstractive summarization of product reviews using discourse structure. In EMNLP, pages 1602–1613. [find](https://scholar.google.com/scholar?q=Shima+Gerani%2C+Yashar+Mehdad%2C+Giuseppe+Carenini%2C+Raymond+T+Ng%2C+and+Bita+Nejat%2E+2014%2E+Abstractive+summarization+of+product+reviews+using+discourse+structure%2E+In+EMNLP%2C+pages+1602%E2%80%931613%2E)
11. Fengyu Guo, Ruifang He, Di Jin, Jianwu Dang, Long-biao Wang, and Xiangang Li. 2018. Implicit discourse relation recognition using neural tensor network with interactive attention and sparse learning. In COLING. [find](https://scholar.google.com/scholar?q=Fengyu+Guo%2C+Ruifang+He%2C+Di+Jin%2C+Jianwu+Dang%2C+Long%2Dbiao+Wang%2C+and+Xiangang+Li%2E+2018%2E+Implicit+discourse+relation+recognition+using+neural+tensor+network+with+interactive+attention+and+sparse+learning%2E+) [our card](https://md.research.elamur.ai/free/en/papers/guo-2018-implicit-discourse-relation-recognition-neural/)
12. Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015. Knowledge graph embedding via dynamic mapping matrix. In ACL, volume 1, pages 687–696. [find](https://scholar.google.com/scholar?q=Guoliang+Ji%2C+Shizhu+He%2C+Liheng+Xu%2C+Kang+Liu%2C+and+Jun+Zhao%2E+2015%2E+Knowledge+graph+embedding+via+dynamic+mapping+matrix%2E+In+ACL%2C+volume+1%2C+pages+687%E2%80%93696%2E) [our card](https://md.research.elamur.ai/free/en/papers/ji-2015-knowledge-graph-embedding-dynamic-mapping/)
13. Yangfeng Ji and Jacob Eisenstein. 2015. One vector is not enough: Entity-augmented distributed semantics for discourse relations. TACL, pages 329–344. [find](https://scholar.google.com/scholar?q=Yangfeng+Ji+and+Jacob+Eisenstein%2E+2015%2E+One+vector+is+not+enough%3A+Entity%2Daugmented+distributed+semantics+for+discourse+relations%2E+TACL%2C+pages+329%E2%80%93344%2E) [our card](https://md.research.elamur.ai/free/en/papers/ji-2015-one-vector-is-not-enough/)
14. Yangfeng Ji, Gholamreza Haffari, and Jacob Eisenstein. 2016. A latent variable recurrent neural network for discourse relation language models. In NAACL-HLT, pages 332–342. [find](https://scholar.google.com/scholar?q=Yangfeng+Ji%2C+Gholamreza+Haffari%2C+and+Jacob+Eisenstein%2E+2016%2E+A+latent+variable+recurrent+neural+network+for+discourse+relation+language+models%2E+In+NAACL%2DHLT%2C+pages+332%E2%80%93342%2E)
15. Yudai Kishimoto, Yugo Murawaki, and Sadao Kurohashi. 2018. A knowledge-augmented neural network model for implicit discourse relation classification. In COLING, pages 584–595. [find](https://scholar.google.com/scholar?q=Yudai+Kishimoto%2C+Yugo+Murawaki%2C+and+Sadao+Kurohashi%2E+2018%2E+A+knowledge%2Daugmented+neural+network+model+for+implicit+discourse+relation+classification%2E+In+COLING%2C+pages+584%E2%80%93595%2E)
16. Jifan Chen, Qi Zhang, Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2016. Implicit discourse relation detection via a deep architecture with gated relevance network. In ACL. [find](https://scholar.google.com/scholar?q=Jifan+Chen%2C+Qi+Zhang%2C+Pengfei+Liu%2C+Xipeng+Qiu%2C+and+Xuanjing+Huang%2E+2016%2E+Implicit+discourse+relation+detection+via+a+deep+architecture+with+gated+relevance+network%2E+In+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/chen-2016-implicit-discourse-relation-detection-deep/)
17. John Lafferty, Andrew McCallum, and Fernando CN Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, 2001, pages 282–289. [find](https://scholar.google.com/scholar?q=John+Lafferty%2C+Andrew+McCallum%2C+and+Fernando+CN+Pereira%2E+2001%2E+Conditional+random+fields%3A+Probabilistic+models+for+segmenting+and+labeling+sequence+data%2E+In+ICML%2C+2001%2C+pages+282%E2%80%93289%2E)
18. Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Diana Inkpen, and Si Wei. 2018. Neural natural language inference models enhanced with external knowledge. In ACL, pages 2406–2417. [find](https://scholar.google.com/scholar?q=Qian+Chen%2C+Xiaodan+Zhu%2C+Zhen%2DHua+Ling%2C+Diana+Inkpen%2C+and+Si+Wei%2E+2018%2E+Neural+natural+language+inference+models+enhanced+with+external+knowledge%2E+In+ACL%2C+pages+2406%E2%80%932417%2E)
19. Man Lan, Jianxiang Wang, Yuanbin Wu, Zheng-Yu Niu, and Haifeng Wang. 2017. Multi-task attention-based neural networks for implicit discourse relationship representation and identification. In EMNLP, pages 1310–1319. [find](https://scholar.google.com/scholar?q=Man+Lan%2C+Jianxiang+Wang%2C+Yuanbin+Wu%2C+Zheng%2DYu+Niu%2C+and+Haifeng+Wang%2E+2017%2E+Multi%2Dtask+attention%2Dbased+neural+networks+for+implicit+discourse+relationship+representation+and+identification%2E+In+EMNLP%2C+p) [our card](https://md.research.elamur.ai/free/en/papers/lan-2017-multi-task-attention-based-neural/)
20. Timothy Chklovski and Patrick Pantel. 2004. Verbocean: Mining the web for fine-grained semantic verb relations. In EMNLP. [find](https://scholar.google.com/scholar?q=Timothy+Chklovski+and+Patrick+Pantel%2E+2004%2E+Verbocean%3A+Mining+the+web+for+fine%2Dgrained+semantic+verb+relations%2E+In+EMNLP%2E)
21. Kevin Clark and Christopher D Manning. 2016. Improving coreference resolution by learning entity-level distributed representations. In ACL, volume 1, pages 643–653. [find](https://scholar.google.com/scholar?q=Kevin+Clark+and+Christopher+D+Manning%2E+2016%2E+Improving+coreference+resolution+by+learning+entity%2Dlevel+distributed+representations%2E+In+ACL%2C+volume+1%2C+pages+643%E2%80%93653%2E) [our card](https://md.research.elamur.ai/free/en/papers/clark-2016-improving-coreference-resolution-learning-entity/)
22. Alexis Conneau, Douwe Kiela, Holger Schwenk, Loı̈c Barrault, and Antoine Bordes. 2017. Supervised learning of universal sentence representations from natural language inference data. In EMNLP, pages 681–691. [find](https://scholar.google.com/scholar?q=Alexis+Conneau%2C+Douwe+Kiela%2C+Holger+Schwenk%2C+Lo%C4%B1%CC%88c+Barrault%2C+and+Antoine+Bordes%2E+2017%2E+Supervised+learning+of+universal+sentence+representations+from+natural+language+inference+data%2E+In+EMNLP%2C+pages+6) [our card](https://md.research.elamur.ai/free/en/papers/conneau-2017-supervised-learning-universal-sentence-representations/)
23. Zeyu Dai and Ruihong Huang. 2018. Improving implicit discourse relation classification by modeling inter-dependencies of discourse units in a paragraph. In NAACL, volume 1, pages 141–151. [find](https://scholar.google.com/scholar?q=Zeyu+Dai+and+Ruihong+Huang%2E+2018%2E+Improving+implicit+discourse+relation+classification+by+modeling+inter%2Ddependencies+of+discourse+units+in+a+paragraph%2E+In+NAACL%2C+volume+1%2C+pages+141%E2%80%93151%2E) [our card](https://md.research.elamur.ai/free/en/papers/dai-2018-improving-implicit-discourse-relation-classification/)
24. Wenqiang Lei, Xuancong Wang, Meichun Liu, Ilija Ilievski, Xiangnan He, and Min-Yen Kan. 2017. Swim: A simple word interaction model for implicit discourse relation recognition. In IJCAI, pages 4026–4032. [find](https://scholar.google.com/scholar?q=Wenqiang+Lei%2C+Xuancong+Wang%2C+Meichun+Liu%2C+Ilija+Ilievski%2C+Xiangnan+He%2C+and+Min%2DYen+Kan%2E+2017%2E+Swim%3A+A+simple+word+interaction+model+for+implicit+discourse+relation+recognition%2E+In+IJCAI%2C+pages+4026%E2%80%9340)
25. Wenqiang Lei, Yuanxin Xiang, Yuwei Wang, Qian Zhong, Meichun Liu, and Min-Yen Kan. 2018. Linguistic properties matter for implicit discourse relation recognition: Combining semantic interaction, topic continuity and attribution. In AAAI. [find](https://scholar.google.com/scholar?q=Wenqiang+Lei%2C+Yuanxin+Xiang%2C+Yuwei+Wang%2C+Qian+Zhong%2C+Meichun+Liu%2C+and+Min%2DYen+Kan%2E+2018%2E+Linguistic+properties+matter+for+implicit+discourse+relation+recognition%3A+Combining+semantic+interaction%2C+topic)
26. Beth Levin. 1993. English verb classes and alternations: A preliminary investigation. University of Chicago press. [find](https://scholar.google.com/scholar?q=Beth+Levin%2E+1993%2E+English+verb+classes+and+alternations%3A+A+preliminary+investigation%2E+University+of+Chicago+press%2E)
27. Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In AAAI. [find](https://scholar.google.com/scholar?q=Yankai+Lin%2C+Zhiyuan+Liu%2C+Maosong+Sun%2C+Yang+Liu%2C+and+Xuan+Zhu%2E+2015%2E+Learning+entity+and+relation+embeddings+for+knowledge+graph+completion%2E+In+AAAI%2E)
28. Ziheng Lin, Min-Yen Kan, and Hwee Tou Ng. 2009. Recognizing implicit discourse relations in the penn discourse treebank. In EMNLP, pages 343–351. [find](https://scholar.google.com/scholar?q=Ziheng+Lin%2C+Min%2DYen+Kan%2C+and+Hwee+Tou+Ng%2E+2009%2E+Recognizing+implicit+discourse+relations+in+the+penn+discourse+treebank%2E+In+EMNLP%2C+pages+343%E2%80%93351%2E) [our card](https://md.research.elamur.ai/free/en/papers/lin-2009-recognizing-implicit-discourse-relations-penn/)
29. Ziheng Lin, Hwee Tou Ng, and Min-Yen Kan. 2014. A pdtb-styled end-to-end discourse parser. Natural Language Engineering, 20(2):151–184. [find](https://scholar.google.com/scholar?q=Ziheng+Lin%2C+Hwee+Tou+Ng%2C+and+Min%2DYen+Kan%2E+2014%2E+A+pdtb%2Dstyled+end%2Dto%2Dend+discourse+parser%2E+Natural+Language+Engineering%2C+20%282%29%3A151%E2%80%93184%2E)
30. Yang Liu and Sujian Li. 2016. Recognizing implicit discourse relations via repeated reading: Neural networks with multi-level attention. In EMNLP, pages 1224–1233. [find](https://scholar.google.com/scholar?q=Yang+Liu+and+Sujian+Li%2E+2016%2E+Recognizing+implicit+discourse+relations+via+repeated+reading%3A+Neural+networks+with+multi%2Dlevel+attention%2E+In+EMNLP%2C+pages+1224%E2%80%931233%2E) [our card](https://md.research.elamur.ai/free/en/papers/liu-2016-recognizing-implicit-discourse-relations-repeated/)
31. Yang Liu, Sujian Li, Xiaodong Zhang, and Zhifang Sui. 2016. Implicit discourse relation classification via multi-task neural networks. In AAAI, pages 2750– 2756. [find](https://scholar.google.com/scholar?q=Yang+Liu%2C+Sujian+Li%2C+Xiaodong+Zhang%2C+and+Zhifang+Sui%2E+2016%2E+Implicit+discourse+relation+classification+via+multi%2Dtask+neural+networks%2E+In+AAAI%2C+pages+2750%E2%80%93+2756%2E)
32. Yukun Ma, Haiyun Peng, and Erik Cambria. 2018. Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive lstm. In AAAI. [find](https://scholar.google.com/scholar?q=Yukun+Ma%2C+Haiyun+Peng%2C+and+Erik+Cambria%2E+2018%2E+Targeted+aspect%2Dbased+sentiment+analysis+via+embedding+commonsense+knowledge+into+an+attentive+lstm%2E+In+AAAI%2E)
33. William C Mann. 1984. Discourse structures for text generation. In 10th International Conference on Computational Linguistics and 22nd Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=William+C+Mann%2E+1984%2E+Discourse+structures+for+text+generation%2E+In+10th+International+Conference+on+Computational+Linguistics+and+22nd+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2E)
34. Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David Mc-Closky. 2014. The Stanford CoreNLP natural language processing toolkit. In ACL System Demonstrations, pages 55–60. [find](https://scholar.google.com/scholar?q=Christopher+D%2E+Manning%2C+Mihai+Surdeanu%2C+John+Bauer%2C+Jenny+Finkel%2C+Steven+J%2E+Bethard%2C+and+David+Mc%2DClosky%2E+2014%2E+The+Stanford+CoreNLP+natural+language+processing+toolkit%2E+In+ACL+System+Demonstrations%2C+) [our card](https://md.research.elamur.ai/free/en/papers/manning-2014-stanford-corenlp-natural-language-processing/)
35. Joonsuk Park and Claire Cardie. 2012. Improving implicit discourse relation recognition through feature set optimization. In Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 108–112. [find](https://scholar.google.com/scholar?q=Joonsuk+Park+and+Claire+Cardie%2E+2012%2E+Improving+implicit+discourse+relation+recognition+through+feature+set+optimization%2E+In+Proceedings+of+the+13th+Annual+Meeting+of+the+Special+Interest+Group+on+Dis) [our card](https://md.research.elamur.ai/free/en/papers/park-2012-improving-implicit-discourse-relation-recognition/)
36. Lianhui Qin, Zhisong Zhang, and Hai Zhao. 2016b. A stacking gated neural architecture for implicit discourse relation classification. In EMNLP, pages 2263–2270. [find](https://scholar.google.com/scholar?q=Lianhui+Qin%2C+Zhisong+Zhang%2C+and+Hai+Zhao%2E+2016b%2E+A+stacking+gated+neural+architecture+for+implicit+discourse+relation+classification%2E+In+EMNLP%2C+pages+2263%E2%80%932270%2E) [our card](https://md.research.elamur.ai/free/en/papers/qin-2016-stacking-gated-neural-architecture-implicit/)
37. Lianhui Qin, Zhisong Zhang, Hai Zhao, Zhiting Hu, and Eric P. Xing. 2017. Adversarial connective-exploiting networks for implicit discourse relation classification. In ACL, pages 1006–1017. [find](https://scholar.google.com/scholar?q=Lianhui+Qin%2C+Zhisong+Zhang%2C+Hai+Zhao%2C+Zhiting+Hu%2C+and+Eric+P%2E+Xing%2E+2017%2E+Adversarial+connective%2Dexploiting+networks+for+implicit+discourse+relation+classification%2E+In+ACL%2C+pages+1006%E2%80%931017%2E) [our card](https://md.research.elamur.ai/free/en/papers/qin-2017-adversarial-connective-exploiting-networks-implicit/)
38. Attapol Rutherford, Vera Demberg, and Nianwen Xue. 2017. A systematic study of neural discourse models for implicit discourse relation. In EACL, pages 281–291. [find](https://scholar.google.com/scholar?q=Attapol+Rutherford%2C+Vera+Demberg%2C+and+Nianwen+Xue%2E+2017%2E+A+systematic+study+of+neural+discourse+models+for+implicit+discourse+relation%2E+In+EACL%2C+pages+281%E2%80%93291%2E) [our card](https://md.research.elamur.ai/free/en/papers/rutherford-2017-systematic-study-neural-discourse-models/)
39. Attapol Rutherford and Nianwen Xue. 2014. Discovering implicit discourse relations through brown cluster pair representation and coreference patterns. In EACL, pages 645–654. [find](https://scholar.google.com/scholar?q=Attapol+Rutherford+and+Nianwen+Xue%2E+2014%2E+Discovering+implicit+discourse+relations+through+brown+cluster+pair+representation+and+coreference+patterns%2E+In+EACL%2C+pages+645%E2%80%93654%2E) [our card](https://md.research.elamur.ai/free/en/papers/rutherford-2014-discovering-implicit-discourse-relations-through/)
40. Attapol Rutherford and Nianwen Xue. 2015. Improving the inference of implicit discourse relations via classifying explicit discourse connectives. In NAACL-HLT, pages 799–808. [find](https://scholar.google.com/scholar?q=Attapol+Rutherford+and+Nianwen+Xue%2E+2015%2E+Improving+the+inference+of+implicit+discourse+relations+via+classifying+explicit+discourse+connectives%2E+In+NAACL%2DHLT%2C+pages+799%E2%80%93808%2E) [our card](https://md.research.elamur.ai/free/en/papers/rutherford-2015-improving-inference-implicit-discourse-relations/)
41. Attapol T Rutherford and Nianwen Xue. 2016. Robust non-explicit neural discourse parser in english and chinese. ACL, page 55. [find](https://scholar.google.com/scholar?q=Attapol+T+Rutherford+and+Nianwen+Xue%2E+2016%2E+Robust+non%2Dexplicit+neural+discourse+parser+in+english+and+chinese%2E+ACL%2C+page+55%2E) [our card](https://md.research.elamur.ai/free/en/papers/rutherford-2016-robust-non-explicit-neural-discourse/)
42. Cicero D Santos and Bianca Zadrozny. 2014. Learning character-level representations for part-of-speech tagging. In ICML, pages 1818–1826. [find](https://scholar.google.com/scholar?q=Cicero+D+Santos+and+Bianca+Zadrozny%2E+2014%2E+Learning+character%2Dlevel+representations+for+part%2Dof%2Dspeech+tagging%2E+In+ICML%2C+pages+1818%E2%80%931826%2E)
43. Mike Schuster and Kuldip K Paliwal. 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11):2673–2681. [find](https://scholar.google.com/scholar?q=Mike+Schuster+and+Kuldip+K+Paliwal%2E+1997%2E+Bidirectional+recurrent+neural+networks%2E+IEEE+Transactions+on+Signal+Processing%2C+45%2811%29%3A2673%E2%80%932681%2E)
44. Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng. 2013. Reasoning with neural tensor networks for knowledge base completion. In NeurIPS, pages 926–934. [find](https://scholar.google.com/scholar?q=Richard+Socher%2C+Danqi+Chen%2C+Christopher+D+Manning%2C+and+Andrew+Ng%2E+2013%2E+Reasoning+with+neural+tensor+networks+for+knowledge+base+completion%2E+In+NeurIPS%2C+pages+926%E2%80%93934%2E)
45. Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In EMNLP, pages 1532–1543. [find](https://scholar.google.com/scholar?q=Jeffrey+Pennington%2C+Richard+Socher%2C+and+Christopher+Manning%2E+2014%2E+Glove%3A+Global+vectors+for+word+representation%2E+In+EMNLP%2C+pages+1532%E2%80%931543%2E)
46. Robert Speer and Catherine Havasi. 2012. Representing general relational knowledge in conceptnet 5. In LREC, pages 3679–3686. [find](https://scholar.google.com/scholar?q=Robert+Speer+and+Catherine+Havasi%2E+2012%2E+Representing+general+relational+knowledge+in+conceptnet+5%2E+In+LREC%2C+pages+3679%E2%80%933686%2E)
47. Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In NAACL-HLT, volume 1, pages 2227– 2237. [find](https://scholar.google.com/scholar?q=Matthew+Peters%2C+Mark+Neumann%2C+Mohit+Iyyer%2C+Matt+Gardner%2C+Christopher+Clark%2C+Kenton+Lee%2C+and+Luke+Zettlemoyer%2E+2018%2E+Deep+contextualized+word+representations%2E+In+NAACL%2DHLT%2C+volume+1%2C+pages+2227%E2%80%93+2237%2E)
48. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NeurIPS, pages 5998–6008. [find](https://scholar.google.com/scholar?q=Ashish+Vaswani%2C+Noam+Shazeer%2C+Niki+Parmar%2C+Jakob+Uszkoreit%2C+Llion+Jones%2C+Aidan+N+Gomez%2C+%C5%81ukasz+Kaiser%2C+and+Illia+Polosukhin%2E+2017%2E+Attention+is+all+you+need%2E+In+NeurIPS%2C+pages+5998%E2%80%936008%2E)
49. Emily Pitler, Annie Louis, and Ani Nenkova. 2009. Automatic sense prediction for implicit discourse relations in text. In ACL, pages 683–691. [find](https://scholar.google.com/scholar?q=Emily+Pitler%2C+Annie+Louis%2C+and+Ani+Nenkova%2E+2009%2E+Automatic+sense+prediction+for+implicit+discourse+relations+in+text%2E+In+ACL%2C+pages+683%E2%80%93691%2E) [our card](https://md.research.elamur.ai/free/en/papers/disc-2009-pitler-automatic-sense-prediction-implicit-discourse/)
50. Emily Pitler and Ani Nenkova. 2009. Using syntax to disambiguate explicit discourse connectives in text. In ACL-IJCNLP, pages 13–16. [find](https://scholar.google.com/scholar?q=Emily+Pitler+and+Ani+Nenkova%2E+2009%2E+Using+syntax+to+disambiguate+explicit+discourse+connectives+in+text%2E+In+ACL%2DIJCNLP%2C+pages+13%E2%80%9316%2E) [our card](https://md.research.elamur.ai/free/en/papers/pitler-2009-syntax-disambiguate-explicit-discourse-connectives/)
51. R. Prasad, N. Dinesh, Lee A., E. Miltsakaki, L. Robaldo, Joshi A., and B. Webber. 2008. The Penn Discourse Treebank 2.0. In lrec2008. [find](https://scholar.google.com/scholar?q=R%2E+Prasad%2C+N%2E+Dinesh%2C+Lee+A%2E%2C+E%2E+Miltsakaki%2C+L%2E+Robaldo%2C+Joshi+A%2E%2C+and+B%2E+Webber%2E+2008%2E+The+Penn+Discourse+Treebank+2%2E0%2E+In+lrec2008%2E) [our card](https://md.research.elamur.ai/free/en/papers/disc-2008-prasad-penn-discourse-treebank-2-0/)
52. Lianhui Qin, Zhisong Zhang, and Hai Zhao. 2016a. Implicit discourse relation recognition with context-aware character-enhanced embeddings. In COL-ING, pages 1914–1924. [find](https://scholar.google.com/scholar?q=Lianhui+Qin%2C+Zhisong+Zhang%2C+and+Hai+Zhao%2E+2016a%2E+Implicit+discourse+relation+recognition+with+context%2Daware+character%2Denhanced+embeddings%2E+In+COL%2DING%2C+pages+1914%E2%80%931924%2E) [our card](https://md.research.elamur.ai/free/en/papers/qin-2016-implicit-discourse-relation-recognition-context/)
53. Bonnie Webber, Andrei Popescu-Belis, and Jörg Tiedemann. 2017. Proceedings of the third workshop on discourse in machine translation. In Proceedings of the Third Workshop on Discourse in Machine Translation. [find](https://scholar.google.com/scholar?q=Bonnie+Webber%2C+Andrei+Popescu%2DBelis%2C+and+Jo%CC%88rg+Tiedemann%2E+2017%2E+Proceedings+of+the+third+workshop+on+discourse+in+machine+translation%2E+In+Proceedings+of+the+Third+Workshop+on+Discourse+in+Machine+Tran)
54. Yang Xu, Yu Hong, Huibin Ruan, Jianmin Yao, Min Zhang, and Guodong Zhou. 2018. Using active learning to expand training data for implicit discourse relation recognition. In EMNLP, pages 725– 731. [find](https://scholar.google.com/scholar?q=Yang+Xu%2C+Yu+Hong%2C+Huibin+Ruan%2C+Jianmin+Yao%2C+Min+Zhang%2C+and+Guodong+Zhou%2E+2018%2E+Using+active+learning+to+expand+training+data+for+implicit+discourse+relation+recognition%2E+In+EMNLP%2C+pages+725%E2%80%93+731%2E) [our card](https://md.research.elamur.ai/free/en/papers/xu-2018-active-learning-expand-training-data/)
55. Zhen Xu, Bingquan Liu, Baoxun Wang, Chengjie Sun, and Xiaolong Wang. 2017. Incorporating loose-structured knowledge into conversation modeling via recall-gate lstm. In IJCNN, pages 3506–3513. IEEE. [find](https://scholar.google.com/scholar?q=Zhen+Xu%2C+Bingquan+Liu%2C+Baoxun+Wang%2C+Chengjie+Sun%2C+and+Xiaolong+Wang%2E+2017%2E+Incorporating+loose%2Dstructured+knowledge+into+conversation+modeling+via+recall%2Dgate+lstm%2E+In+IJCNN%2C+pages+3506%E2%80%933513%2E+IEEE%2E)
56. Nianwen Xue, Hwee Tou Ng, Sameer Pradhan, Rashmi PrasadO Christopher Bryant, and Attapol T Rutherford. 2015. The conll-2015 shared task on shallow discourse parsing. CoNLL, page 1. [find](https://scholar.google.com/scholar?q=Nianwen+Xue%2C+Hwee+Tou+Ng%2C+Sameer+Pradhan%2C+Rashmi+PrasadO+Christopher+Bryant%2C+and+Attapol+T+Rutherford%2E+2015%2E+The+conll%2D2015+shared+task+on+shallow+discourse+parsing%2E+CoNLL%2C+page+1%2E) [our card](https://md.research.elamur.ai/free/en/papers/xue-2015-conll-2015-shared-task-shallow/)
57. Bishan Yang and Tom Mitchell. 2017. Leveraging knowledge bases in lstms for improving machine reading. In ACL, volume 1, pages 1436–1446. [find](https://scholar.google.com/scholar?q=Bishan+Yang+and+Tom+Mitchell%2E+2017%2E+Leveraging+knowledge+bases+in+lstms+for+improving+machine+reading%2E+In+ACL%2C+volume+1%2C+pages+1436%E2%80%931446%2E)
58. Wenlin Yao and Ruihong Huang. 2018. Temporal event knowledge acquisition via identifying narratives. In ACL, volume 1, pages 537–547. [find](https://scholar.google.com/scholar?q=Wenlin+Yao+and+Ruihong+Huang%2E+2018%2E+Temporal+event+knowledge+acquisition+via+identifying+narratives%2E+In+ACL%2C+volume+1%2C+pages+537%E2%80%93547%2E)
59. Hao Zhou, Tom Young, Minlie Huang, Haizhou Zhao, Jingfang Xu, and Xiaoyan Zhu. 2018. Commonsense knowledge aware conversation generation with graph attention. In IJCAI, pages 4623–4629. [find](https://scholar.google.com/scholar?q=Hao+Zhou%2C+Tom+Young%2C+Minlie+Huang%2C+Haizhou+Zhao%2C+Jingfang+Xu%2C+and+Xiaoyan+Zhu%2E+2018%2E+Commonsense+knowledge+aware+conversation+generation+with+graph+attention%2E+In+IJCAI%2C+pages+4623%E2%80%934629%2E)