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# Revisiting Shallow Discourse Parsing in the PDTB-3: Handling Intra-sentential Implicits

**Authors:** Zheng Zhao, [Bonnie Webber](https://md.mova.elamur.ai/free/en/docs/papers/authors/bonnie-webber/) · **Year:** 2021 · **Venue:** CODI 2021 **Link:** [aclanthology.org/2021.codi-main.10](https://aclanthology.org/2021.codi-main.10/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (paper); PDTB-3 is LDC data

[**3** citations](https://openalex.org/works?filter=cites:W3211954325) openalex-snapshot · 2026-10-14[**3** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zhao-2021-revisiting-shallow-discourse-parsing-pdtb/) of the 140673 papers whose bibliographies we read[Proceedings of the 2nd Workshop on Computational Approaches to Discourse](https://aclanthology.org/volumes/2021.codi-main/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2021.codi-main.10/)
- [PDF](https://aclanthology.org/2021.codi-main.10.pdf)
- [DOI 10.18653/v1/2021.codi-main.10](https://doi.org/10.18653/v1/2021.codi-main.10)
- [arXiv 2204.00350](https://arxiv.org/abs/2204.00350)
- [OpenAlex](https://openalex.org/W3211954325)

**Authors (2)**

1. [Zheng Zhao](https://openalex.org/A5007396467) [ORCID](https://orcid.org/0000-0001-9586-268X) · h 19 · 1362 citations · 111 works
2. [Bonnie Webber](https://openalex.org/A5089032031) [ORCID](https://orcid.org/0000-0002-4284-8216) · h 47 · 9455 citations · 361 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

PDTB-3 added several thousand implicit relations inside sentences (6,234 of 24,369 intra-sentential relations in 46,430 sentences; about 9% of sentences have one, under 1% more than one). Their arguments are not fixed by adjacency: they can come in either order («To slow the rise in spending, it will be necessary to …» has the condition first) and need not cover the sentence. The paper tags argument spans with BIO labels (BiLSTM or BERT with a CRF, optionally with linearised gold parse trees) and classifies the level-2 sense of the found pair with BERT. Fine-tuned BERT with tree features is best: exact-match F1 50.4 for Arg1 and 57.3 for Arg2; tree features mostly raise precision. Positions are skewed: 69% of relations have an argument ending at the sentence end (90% of those Arg2), and 50% one starting at the sentence start (87% of those Arg1); the rarer Arg2-before-Arg1 order is hardest (F1 26–68).

## Ideas

- I1. For Mova the span problem disappears: an intra-sentential implicit relation holds between two frames of one tree joined without a connective — an `advcl` with no `mark` (participial or infinitival adjunct), `parataxis`, or a `conj` of verbs. Count these configurations in the tale corpus and in the frame bench lines, with the share that currently gets a `cause`/`next` link, to know how much the PDTB-3 additions matter for Mova before writing rules (see `prasad-2018-discourse-annotation-pdtb-next-generation` I1).
- I2. Use position as a cheap default for the order and sense of unmarked adjuncts, as the corpus statistics suggest: a sentence-initial *to*-infinitive or participial clause is usually the first argument (purpose/condition or circumstance), a sentence-final participial clause is usually the second (result or manner: «He fell, breaking his leg»). Encode as two named rules in `prag/src/frames.rs` with `PRAG_OFF` switches; measure frame bench cause F1 and course *why* questions.

## For Mova

[36. Discourse relations and coherence](https://md.mova.elamur.ai/free/en/docs/languages/en/36-discourse-relations/) §7 (implicit relations), §10, §11.1. Code: `prag/src/frames.rs` (links for unmarked adjuncts), [Infinitive of purpose — an adverbial (advcl), not a complement](https://md.mova.elamur.ai/free/en/docs/en/seeds/verbal/purpose-clause/), [Absolute construction and independent participles](https://md.mova.elamur.ai/free/en/docs/en/seeds/verbal/absolute-construction/). Measures: configuration counts, frame bench cause/next F1, course why-questions.

Topics: [Discourse analysis](https://md.mova.elamur.ai/free/en/docs/papers/topics/discourse-analysis/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2021.codi-main.10](https://doi.org/10.18653/v1/2021.codi-main.10) · [arXiv 2204.00350](https://arxiv.org/abs/2204.00350)

```
@inproceedings{zhao2021revisiting,
  title = {{Revisiting Shallow Discourse Parsing in the PDTB-3: Handling Intra-sentential Implicits}},
  author = {Zhao, Zheng and Webber, Bonnie},
  booktitle = {Proceedings of the 2nd Workshop on Computational Approaches to Discourse},
  year = {2021},
  doi = {10.18653/v1/2021.codi-main.10},
  eprint = {2204.00350},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2021.codi-main.10}
}
```

- [← previous Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank](https://md.mova.elamur.ai/free/en/docs/papers/zhao-2017-joint-syntacto-discourse-parsing-syntacto/)
- [next → PCR4ALL: A Comprehensive Evaluation Benchmark for Pronoun Coreference Resolution in English](https://md.mova.elamur.ai/free/en/docs/papers/zhao-2022-pcr4all-comprehensive-evaluation-benchmark-pronoun/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

38 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Hongxiao Bai and Hai Zhao. 2018. Deep enhanced representation for implicit discourse relation recognition. In Proceedings of the 27th International Conference on Computational Linguistics, pages 571– 583, Santa Fe, New Mexico, USA. Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.1807.05154)
2. Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018. A discourse-aware attention model for abstractive summarization of long documents. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 615–621, New Orleans, Louisiana. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Arman+Cohan%2C+Franck+Dernoncourt%2C+Doo+Soon+Kim%2C+Trung+Bui%2C+Seokhwan+Kim%2C+Walter+Chang%2C+and+Nazli+Goharian%2E+2018%2E+A+discourse%2Daware+attention+model+for+abstractive+summarization+of+long+documents%2E+In+Pr)
3. Zeyu Dai and Ruihong Huang. 2018. Improving implicit discourse relation classification by modeling inter-dependencies of discourse units in a paragraph. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 141–151, New Orleans, Louisiana. Association for Computational Linguistics. [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+Proceedings+of+the+2018+Conference+of+the+No)
4. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics. [↗](https://doi.org/10.18653/v1/n19-1423)
5. Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew E. Peters, Michael Schmitz, and Luke Zettlemoyer. 2018. Allennlp: A deep semantic natural language processing platform. CoRR, abs/1803.07640. [↗](https://arxiv.org/abs/1803.07640)
6. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput., 9(8):1735–1780. [↗](https://doi.org/10.1162/neco.1997.9.8.1735)
7. Peter Jansen, Mihai Surdeanu, and Peter Clark. 2014. Discourse complements lexical semantics for non-factoid answer reranking. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 977–986, Baltimore, Maryland. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Peter+Jansen%2C+Mihai+Surdeanu%2C+and+Peter+Clark%2E+2014%2E+Discourse+complements+lexical+semantics+for+non%2Dfactoid+answer+reranking%2E+In+Proceedings+of+the+52nd+Annual+Meeting+of+the+Association+for+Computat)
8. Shafiq Joty, Giuseppe Carenini, and Raymond Ng. 2012. A novel discriminative framework for sentence-level discourse analysis. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 904–915, Jeju Island, Korea. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Shafiq+Joty%2C+Giuseppe+Carenini%2C+and+Raymond+Ng%2E+2012%2E+A+novel+discriminative+framework+for+sentence%2Dlevel+discourse+analysis%2E+In+Proceedings+of+the+2012+Joint+Conference+on+Empirical+Methods+in+Natura)
9. Najoung Kim, Song Feng, Chulaka Gunasekara, and Luis Lastras. 2020. Implicit discourse relation classification: We need to talk about evaluation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5404– 5414, Online. Association for Computational Linguistics. [↗](https://doi.org/10.18653/v1/2020.acl-main.480) [our card](https://md.mova.elamur.ai/free/en/docs/papers/kim-2020-implicit-discourse-relation-classification-we/)
10. Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings. [↗](https://doi.org/10.48550/arxiv.1412.6980)
11. Murathan Kurfalı and Robert Östling. 2019. Zero-shot transfer for implicit discourse relation classification. In Proceedings of the 20th Annual SIGdial Meeting Yang, Zachary DeVito, Martin Raison, Alykhan Te-jani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc. on Discourse and Dialogue, pages 226–231, Stockholm, Sweden. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Murathan+Kurfal%C4%B1+and+Robert+%C3%96stling%2E+2019%2E+Zero%2Dshot+transfer+for+implicit+discourse+relation+classification%2E+In+Proceedings+of+the+20th+Annual+SIGdial+Meeting+Yang%2C+Zachary+DeVito%2C+Martin+Raison%2C+Aly)
12. John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 282–289. Morgan Kaufmann. [find](https://scholar.google.com/scholar?q=John+D%2E+Lafferty%2C+Andrew+McCallum%2C+and+Fernando+C%2E+N%2E+Pereira%2E+2001%2E+Conditional+random+fields%3A+Probabilistic+models+for+segmenting+and+labeling+sequence+data%2E+In+Proceedings+of+the+Eighteenth+Interna)
13. Alan Lee, Rashmi Prasad, Aravind Joshi, Nikhil Dinesh, and Bonnie Webber. 2006. Complexity of dependencies in discourse: Are dependencies in discourse more complex than in syntax. In Proceedings of the 5th International Workshop on Treebanks and Linguistic Theories, pages 12–23. [find](https://scholar.google.com/scholar?q=Alan+Lee%2C+Rashmi+Prasad%2C+Aravind+Joshi%2C+Nikhil+Dinesh%2C+and+Bonnie+Webber%2E+2006%2E+Complexity+of+dependencies+in+discourse%3A+Are+dependencies+in+discourse+more+complex+than+in+syntax%2E+In+Proceedings+of+th)
14. Junyi Jessy Li, Marine Carpuat, and Ani Nenkova. 2014. Assessing the discourse factors that influence the quality of machine translation. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 283–288, Baltimore, Maryland. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Junyi+Jessy+Li%2C+Marine+Carpuat%2C+and+Ani+Nenkova%2E+2014%2E+Assessing+the+discourse+factors+that+influence+the+quality+of+machine+translation%2E+In+Proceedings+of+the+52nd+Annual+Meeting+of+the+Association+f)
15. Li Liang, Zheng Zhao, and Bonnie Webber. 2020. Extending implicit discourse relation recognition to the PDTB-3. In Proceedings of the First Workshop on Computational Approaches to Discourse, pages 135–147, Online. Association for Computational Linguistics. [↗](https://doi.org/10.18653/v1/2020.codi-1.14)
16. 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)
17. Yang Liu and Sujian Li. 2016. Recognizing implicit discourse relations via repeated reading: Neural networks with multi-level attention. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1224–1233, Austin, Texas. Association for Computational Linguistics. [↗](https://doi.org/10.60692/tdwhm-fmp75)
18. William C Mann and Sandra A Thompson. 1988. Rhetorical structure theory: Toward a functional theory of text organization. Text, 8(3):243–281. [↗](https://doi.org/10.1515/text.1.1988.8.3.243) [our card](https://md.mova.elamur.ai/free/en/docs/papers/enud-1988-mann-rhetorical-structure-theory-functional/)
19. Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993. Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19(2):313–330. [↗](https://doi.org/10.21236/ada273556) [our card](https://md.mova.elamur.ai/free/en/docs/papers/bank-1993-marcus-building-large-annotated-corpus-english/)
20. Philippe Muller, Stergos Afantenos, Pascal Denis, and Nicholas Asher. 2012. Constrained decoding for text-level discourse parsing. In Proceedings of COL-ING 2012, pages 1883–1900, Mumbai, India. [find](https://scholar.google.com/scholar?q=Philippe+Muller%2C+Stergos+Afantenos%2C+Pascal+Denis%2C+and+Nicholas+Asher%2E+2012%2E+Constrained+decoding+for+text%2Dlevel+discourse+parsing%2E+In+Proceedings+of+COL%2DING+2012%2C+pages+1883%E2%80%931900%2C+Mumbai%2C+India%2E)
21. The COLING 2012 Organizing Committee. [find](https://scholar.google.com/scholar?q=The+COLING+2012+Organizing+Committee%2E)
22. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543. [find](https://scholar.google.com/scholar?q=Adam+Paszke%2C+Sam+Gross%2C+Francisco+Massa%2C+Adam+Lerer%2C+James+Bradbury%2C+Gregory+Chanan%2C+Trevor+Killeen%2C+Zeming+Lin%2C+Natalia+Gimelshein%2C+Luca+Antiga%2C+Alban+Desmaison%2C+Andreas+Kopf%2C+Edward+Jeffrey+Penningt)
23. Rashmi Prasad, Nikhil Dinesh, Alan Lee, Eleni Milt-sakaki, Livio Robaldo, Aravind Joshi, and Bonnie Webber. 2008. The Penn Discourse TreeBank 2.0. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08), Marrakech, Morocco. European Language Resources Association (ELRA). [↗](https://doi.org/10.63317/55sr8v9kexr7) [our card](https://md.mova.elamur.ai/free/en/docs/papers/disc-2008-prasad-penn-discourse-treebank-2-0/)
24. Rashmi Prasad, Katherine Forbes Riley, and Alan Lee. 2017. Towards full text shallow discourse relation annotation: Experiments with cross-paragraph implicit relations in the PDTB. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pages 7–16, Saarbrücken, Germany. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Rashmi+Prasad%2C+Katherine+Forbes+Riley%2C+and+Alan+Lee%2E+2017%2E+Towards+full+text+shallow+discourse+relation+annotation%3A+Experiments+with+cross%2Dparagraph+implicit+relations+in+the+PDTB%2E+In+Proceedings+of+t)
25. Rashmi Prasad, Aravind Joshi, and Bonnie Webber. 2010. Realization of discourse relations by other means: Alternative lexicalizations. In Coling 2010: Posters, pages 1023–1031, Beijing, China. Coling 2010 Organizing Committee. [find](https://scholar.google.com/scholar?q=Rashmi+Prasad%2C+Aravind+Joshi%2C+and+Bonnie+Webber%2E+2010%2E+Realization+of+discourse+relations+by+other+means%3A+Alternative+lexicalizations%2E+In+Coling+2010%3A+Posters%2C+pages+1023%E2%80%931031%2C+Beijing%2C+China%2E+Coling+)
26. Lance A Ramshaw and Mitchell P Marcus. 1999. Text chunking using transformation-based learning. In Natural language processing using very large corpora, pages 157–176. Springer. [↗](https://doi.org/10.1007/978-94-017-2390-9_10)
27. Wei Shi and Vera Demberg. 2017. On the need of cross validation for discourse relation classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 150–156, Valencia, Spain. Association for Computational Linguistics. [↗](https://doi.org/10.22028/d291-30985)
28. Wei Shi and Vera Demberg. 2019. Next sentence prediction helps implicit discourse relation classification within and across domains. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5790–5796, Hong Kong, China. Association for Computational Linguistics. [↗](https://doi.org/10.22028/d291-30791)
29. Manfred Stede. 2011. Discourse processing. Synthesis Lectures on Human Language Technologies, 4(3):1– 165. [find](https://scholar.google.com/scholar?q=Manfred+Stede%2E+2011%2E+Discourse+processing%2E+Synthesis+Lectures+on+Human+Language+Technologies%2C+4%283%29%3A1%E2%80%93+165%2E)
30. Manfred Stede, Stergos Afantenos, Andreas Peldszus, Nicholas Asher, and Jérémy Perret. 2016. Parallel discourse annotations on a corpus of short texts. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 1051–1058, Portorož, Slovenia. European Language Resources Association (ELRA). [find](https://scholar.google.com/scholar?q=Manfred+Stede%2C+Stergos+Afantenos%2C+Andreas+Peldszus%2C+Nicholas+Asher%2C+and+J%C3%A9r%C3%A9my+Perret%2E+2016%2E+Parallel+discourse+annotations+on+a+corpus+of+short+texts%2E+In+Proceedings+of+the+Tenth+International+Confer)
31. Siddharth Varia, Christopher Hidey, and Tuhin Chakrabarty. 2019. Discourse relation prediction: Revisiting word pairs with convolutional networks. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 442–452, Stockholm, Sweden. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Siddharth+Varia%2C+Christopher+Hidey%2C+and+Tuhin+Chakrabarty%2E+2019%2E+Discourse+relation+prediction%3A+Revisiting+word+pairs+with+convolutional+networks%2E+In+Proceedings+of+the+20th+Annual+SIGdial+Meeting+on+)
32. Jianxiang Wang and Man Lan. 2015. A refined end-to-end discourse parser. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task, pages 17–24, Beijing, China. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Jianxiang+Wang+and+Man+Lan%2E+2015%2E+A+refined+end%2Dto%2Dend+discourse+parser%2E+In+Proceedings+of+the+Nineteenth+Conference+on+Computational+Natural+Language+Learning+%2D+Shared+Task%2C+pages+17%E2%80%9324%2C+Beijing%2C+Chi)
33. Bonnie Webber, Rashmi Prasad, Alan Lee, and Aravind Joshi. 2019. The penn discourse treebank 3.0 annotation manual. [https://catalog.ldc.upenn.edu/docs/LDC2019T05/](https://catalog.ldc.upenn.edu/docs/LDC2019T05/) PDTB3-Annotation-Manual.pdf. [↗](https://catalog.ldc.upenn.edu/docs/LDC2019T05/) [our card](https://md.mova.elamur.ai/free/en/docs/papers/miltsakaki-2004-penn-discourse-treebank/)
34. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier-ric Cistac, Tim Rault, Remi Louf, Morgan Funtow-icz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Thomas+Wolf%2C+Lysandre+Debut%2C+Victor+Sanh%2C+Julien+Chaumond%2C+Clement+Delangue%2C+Anthony+Moi%2C+Pier%2Dric+Cistac%2C+Tim+Rault%2C+Remi+Louf%2C+Morgan+Funtow%2Dicz%2C+Joe+Davison%2C+Sam+Shleifer%2C+Patrick+von+Platen%2C+Clara)
35. Nianwen Xue, Hwee Tou Ng, Sameer Pradhan, Rashmi Prasad, Christopher Bryant, and Attapol Rutherford. 2015. The CoNLL-2015 shared task on shallow discourse parsing. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task, pages 1–16, Beijing, China. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Nianwen+Xue%2C+Hwee+Tou+Ng%2C+Sameer+Pradhan%2C+Rashmi+Prasad%2C+Christopher+Bryant%2C+and+Attapol+Rutherford%2E+2015%2E+The+CoNLL%2D2015+shared+task+on+shallow+discourse+parsing%2E+In+Proceedings+of+the+Nineteenth+Con)
36. Nianwen Xue, Hwee Tou Ng, Sameer Pradhan, Attapol Rutherford, Bonnie Webber, Chuan Wang, and Hongmin Wang. 2016. CoNLL 2016 shared task on multilingual shallow discourse parsing. In Proceedings of the CoNLL-16 shared task, pages 1– 19, Berlin, Germany. Association for Computational Linguistics. [↗](https://doi.org/10.18653/v1/k16-2001) [our card](https://md.mova.elamur.ai/free/en/docs/papers/xue-2016-conll-2016-shared-task-multilingual/)
37. Amir Zeldes, Debopam Das, Erick Galani Maziero, Juliano Antonio, and Mikel Iruskieta. 2019. [find](https://scholar.google.com/scholar?q=Amir+Zeldes%2C+Debopam+Das%2C+Erick+Galani+Maziero%2C+Juliano+Antonio%2C+and+Mikel+Iruskieta%2E+2019%2E)
38. The DIS-RPT 2019 shared task on elementary discourse unit segmentation and connective detection. In Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019, pages 97–104, Minneapolis, MN. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=The+DIS%2DRPT+2019+shared+task+on+elementary+discourse+unit+segmentation+and+connective+detection%2E+In+Proceedings+of+the+Workshop+on+Discourse+Relation+Parsing+and+Treebanking+2019%2C+pages+97%E2%80%93104%2C+Minnea)