---
title: "Top-down Discourse Parsing via Sequence — Koto 2021"
description: "The paper addresses document-level discourse parsing under Rhetorical Structure Theory (RST)."
authors: ["Fajri Koto", "Jey Han Lau", "Timothy Baldwin"]
year: 2021
venue: "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume"
source: "https://aclanthology.org/2021.eacl-main.60/"
doi: "10.18653/v1/2021.eacl-main.60"
arxiv: "2102.02080"
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/koto-2021-top-down-discourse-parsing-sequence/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/koto-2021-top-down-discourse-parsing-sequence/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2021](https://md.research.elamur.ai/free/en/papers/2021/page/4/) / Top-down Discourse Parsing via Sequence Labelling

# Top-down Discourse Parsing via Sequence Labelling

**Authors:** [Fajri Koto](https://md.research.elamur.ai/free/en/authors/fajri-koto/), [Jey Han Lau](https://md.research.elamur.ai/free/en/authors/jey-han-lau/), [Timothy Baldwin](https://md.research.elamur.ai/free/en/authors/timothy-baldwin/) · **Year:** 2021 · **Venue:** Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.eacl-main.60/) · [PDF](https://aclanthology.org/2021.eacl-main.60.pdf) · [DOI 10.18653/v1/2021.eacl-main.60](https://doi.org/10.18653/v1/2021.eacl-main.60) · [arXiv 2102.02080](https://arxiv.org/abs/2102.02080) · [OpenAlex](https://openalex.org/W3153791507)

**Citations:** 19 citations (openalex-snapshot · 2026-10-14) · [18](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses document-level discourse parsing under Rhetorical Structure Theory (RST). Most earlier parsers build trees bottom-up, which tends to ignore global document context. Recent top-down parsers have relied on complex machinery such as decoders, stacks or exhaustive split scoring. The authors recast top-down parsing as sequence labelling. Given a span of EDUs, a model assigns each EDU a binary label and treats the most probable one as the split point. It then recursively divides the resulting halves until only single units remain. This design removes the decoder and narrows the set of candidate splits.

Two segmenters are compared:
- a stacked Bi-LSTM encoder that uses word, POS and dependency-syntax features;
- a BERT-based transformer that processes long documents as overlapping partial chunks.

The authors also add a split-weighting mechanism and a new dynamic oracle for training. On RST-DT, the LSTM variant achieves state-of-the-art results on the Full metric and beats the BERT version. This shows that a simpler architecture can compete with more complex parsers. Code and models are publicly released.

## Key points
- Evaluation uses the RST Discourse Treebank (RST-DT) with 18 coarse-grained relations and right-heavy binarized trees. Scoring follows micro-averaged Parseval-style F1, as recommended by Morey et al. (2017).
- Documents average 521 words (741 word pieces), which exceeds BERT's 512-token limit. The transformer model therefore splits documents into partial documents. The authors suggest this, together with BERT's pre-training on short texts, may explain why it trails the LSTM.
- Without paragraph-boundary features, the model improves on Zhang et al. (2020) by +0.1 (Span), +1.9 (Nuclearity), +3.2 (Relation) and +3.1 (Full).
- The dynamic oracle mainly helps on longer documents, such as those with 50–100 EDUs. Static training works better on short ones.
- In the error analysis, the best model recovers 1,698 of 2,308 gold spans. Nucleus–Nucleus pairs are often mislabelled as NS, reflecting a training ratio of NN:NS:SN = 23:61:16. The elab relation (37% of the data) has only 74% precision, while attr reaches 94% precision and 96% recall. Code: github.com/fajri91/NeuralRST-TopDown.

*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/)

## Cited by

Cited by

18 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 13 not yet.

- [Bilingual Rhetorical Structure Parsing with Large Parallel Annotations](https://md.research.elamur.ai/free/en/papers/chistova-2024-bilingual-rhetorical-structure-parsing-large/)
- [A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing](https://md.research.elamur.ai/free/en/papers/kobayashi-2022-simple-strong-baseline-end-end/)
- [Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity](https://md.research.elamur.ai/free/en/papers/liu-2023-why-can-t-discourse-parsing/)
- [RST Discourse Parsing with Second-Stage EDU-Level Pre-training](https://md.research.elamur.ai/free/en/papers/yu-2022-rst-discourse-parsing-second-stage/)
- [Adversarial Learning for Discourse Rhetorical Structure Parsing](https://md.research.elamur.ai/free/en/papers/zhang-2021-adversarial-learning-discourse-rhetorical-structure/)

## Cite

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

```bibtex
@inproceedings{koto2021top,
  title = {{Top-down Discourse Parsing via Sequence Labelling}},
  author = {Koto, Fajri and Lau, Jey Han and Baldwin, Timothy},
  booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
  year = {2021},
  doi = {10.18653/v1/2021.eacl-main.60},
  eprint = {2102.02080},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2021.eacl-main.60}
}
```

- [← previous Thematic role assignment in non-default verb classes: A cross-linguistic comparison of English and German](https://md.research.elamur.ai/free/en/papers/kyriaki-2021-thematic-role-assignment-non-default/)
- [next → Toward a Perspectivist Turn in Ground Truthing for Predictive Computing](https://md.research.elamur.ai/free/en/papers/basile-2021-toward-perspectivist-turn-ground-truthing/)
- [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/4/)

## References

43 references, 23 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Parminder Bhatia, Yangfeng Ji, and Jacob Eisenstein. 2015. Better document-level sentiment analysis from RST discourse parsing. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2212–2218. [find](https://scholar.google.com/scholar?q=Parminder+Bhatia%2C+Yangfeng+Ji%2C+and+Jacob+Eisenstein%2E+2015%2E+Better+document%2Dlevel+sentiment+analysis+from+RST+discourse+parsing%2E+In+Proceedings+of+the+2015+Conference+on+Empirical+Methods+in+Natural+La) [our card](https://md.research.elamur.ai/free/en/papers/bhatia-2015-better-document-level-sentiment-analysis/)
2. Chloé Braud, Maximin Coavoux, and Anders Søgaard. 2017. Cross-lingual RST discourse parsing. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 292–304, Valencia, Spain. Association for Computational Linguisti… [find](https://scholar.google.com/scholar?q=Chloe%CC%81+Braud%2C+Maximin+Coavoux%2C+and+Anders+S%C3%B8gaard%2E+2017%2E+Cross%2Dlingual+RST+discourse+parsing%2E+In+Proceedings+of+the+15th+Conference+of+the+European+Chapter+of+the+Association+for+Computational+Linguis) [our card](https://md.research.elamur.ai/free/en/papers/braud-2017-cross-lingual-rst-discourse-parsing/)
3. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation, 9:1735– 1780. [find](https://scholar.google.com/scholar?q=Sepp+Hochreiter+and+Ju%CC%88rgen+Schmidhuber%2E+1997%2E+Long+short%2Dterm+memory%2E+Neural+Computation%2C+9%3A1735%E2%80%93+1780%2E)
4. Chloé Braud, Barbara Plank, and Anders Søgaard. 2016. Multi-view and multi-task training of RST discourse parsers. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1903–1913, Osaka, Japan. [find](https://scholar.google.com/scholar?q=Chloe%CC%81+Braud%2C+Barbara+Plank%2C+and+Anders+S%C3%B8gaard%2E+2016%2E+Multi%2Dview+and+multi%2Dtask+training+of+RST+discourse+parsers%2E+In+Proceedings+of+COLING+2016%2C+the+26th+International+Conference+on+Computational+Li)
5. The COLING 2016 Organizing Committee. [find](https://scholar.google.com/scholar?q=The+COLING+2016+Organizing+Committee%2E)
6. Ganesh Jawahar, Benoı̂t Sagot, and Djamé Seddah. 2019. What does BERT learn about the structure of language? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3651–3657, Florence, Italy. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Ganesh+Jawahar%2C+Beno%C4%B1%CC%82t+Sagot%2C+and+Djame%CC%81+Seddah%2E+2019%2E+What+does+BERT+learn+about+the+structure+of+language%3F+In+Proceedings+of+the+57th+Annual+Meeting+of+the+Association+for+Computational+Linguistics) [our card](https://md.research.elamur.ai/free/en/papers/jawahar-2019-what-does-bert-learn-about/)
7. Lynn Carlson, Daniel Marcu, and Mary Ellen Okurowski. 2001. Building a discourse-tagged corpus in the framework of rhetorical structure theory. In SIGDIAL ’01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16, pages 1–10. [find](https://scholar.google.com/scholar?q=Lynn+Carlson%2C+Daniel+Marcu%2C+and+Mary+Ellen+Okurowski%2E+2001%2E+Building+a+discourse%2Dtagged+corpus+in+the+framework+of+rhetorical+structure+theory%2E+In+SIGDIAL+%E2%80%9901+Proceedings+of+the+Second+SIGdial+Worksho) [our card](https://md.research.elamur.ai/free/en/papers/carlson-2001-building-discourse-tagged-corpus-framework/)
8. Yangfeng Ji and Jacob Eisenstein. 2014. Representation learning for text-level discourse parsing. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13–24, Baltimore, Maryland. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Yangfeng+Ji+and+Jacob+Eisenstein%2E+2014%2E+Representation+learning+for+text%2Dlevel+discourse+parsing%2E+In+Proceedings+of+the+52nd+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volume+1%3A+) [our card](https://md.research.elamur.ai/free/en/papers/ji-2014-representation-learning-text-level-discourse/)
9. 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,… [find](https://scholar.google.com/scholar?q=Jacob+Devlin%2C+Ming%2DWei+Chang%2C+Kenton+Lee%2C+and+Kristina+Toutanova%2E+2019%2E+BERT%3A+Pre%2Dtraining+of+deep+bidirectional+transformers+for+language+understanding%2E+In+Proceedings+of+the+2019+Conference+of+the+N)
10. Mark Johnson. 1995. Squibs and discussions: Mem-oization in top-down parsing. Computational Linguistics, 21(3):405–417. [find](https://scholar.google.com/scholar?q=Mark+Johnson%2E+1995%2E+Squibs+and+discussions%3A+Mem%2Doization+in+top%2Ddown+parsing%2E+Computational+Linguistics%2C+21%283%29%3A405%E2%80%93417%2E)
11. Timothy Dozat and Christopher D. Manning. 2017. Deep biaffine attention for neural dependency parsing. In Proceedings of the 2016 International Conference on Learning Representations, pages 1–8. [find](https://scholar.google.com/scholar?q=Timothy+Dozat+and+Christopher+D%2E+Manning%2E+2017%2E+Deep+biaffine+attention+for+neural+dependency+parsing%2E+In+Proceedings+of+the+2016+International+Conference+on+Learning+Representations%2C+pages+1%E2%80%938%2E) [our card](https://md.research.elamur.ai/free/en/papers/missing-2016-dozat-deep-biaffine-attention-neural-dependency/)
12. Vanessa Wei Feng and Graeme Hirst. 2014. A linear-time bottom-up discourse parser with constraints and post-editing. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 511– 521, Baltimore, Maryland. Association for Computational… [find](https://scholar.google.com/scholar?q=Vanessa+Wei+Feng+and+Graeme+Hirst%2E+2014%2E+A+linear%2Dtime+bottom%2Dup+discourse+parser+with+constraints+and+post%2Dediting%2E+In+Proceedings+of+the+52nd+Annual+Meeting+of+the+Association+for+Computational+Ling) [our card](https://md.research.elamur.ai/free/en/papers/feng-2014-linear-time-bottom-up-discourse/)
13. Shafiq Joty, Giuseppe Carenini, Raymond Ng, and Yashar Mehdad. 2013. Combining intra- and multi-sentential rhetorical parsing for document-level discourse analysis. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 486–496, Sofi… [find](https://scholar.google.com/scholar?q=Shafiq+Joty%2C+Giuseppe+Carenini%2C+Raymond+Ng%2C+and+Yashar+Mehdad%2E+2013%2E+Combining+intra%2D+and+multi%2Dsentential+rhetorical+parsing+for+document%2Dlevel+discourse+analysis%2E+In+Proceedings+of+the+51st+Annual+M)
14. Shafiq Joty, Giuseppe Carenini, and Raymond T. Ng. 2015. CODRA: A novel discriminative framework for rhetorical analysis. Computational Linguistics, 41(3):385–435. [find](https://scholar.google.com/scholar?q=Shafiq+Joty%2C+Giuseppe+Carenini%2C+and+Raymond+T%2E+Ng%2E+2015%2E+CODRA%3A+A+novel+discriminative+framework+for+rhetorical+analysis%2E+Computational+Linguistics%2C+41%283%29%3A385%E2%80%93435%2E) [our card](https://md.research.elamur.ai/free/en/papers/joty-2015-codra-novel-discriminative-framework-rhetorical/)
15. Naoki Kobayashi, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, and Masaaki Nagata. 2020. Top-down RST parsing utilizing granularity levels in documents. In AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Naoki+Kobayashi%2C+Tsutomu+Hirao%2C+Hidetaka+Kamigaito%2C+Manabu+Okumura%2C+and+Masaaki+Nagata%2E+2020%2E+Top%2Ddown+RST+parsing+utilizing+granularity+levels+in+documents%2E+In+AAAI+2020+%3A+The+Thirty%2DFourth+AAAI+Conf)
16. Richard Frost, Rahmatullah Hafiz, and Paul Callaghan. 2007. Modular and efficient top-down parsing for ambiguous left-recursive grammars. In Proceedings of the Tenth International Conference on Parsing Technologies, pages 109–120, Prague, Czech Republic. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Richard+Frost%2C+Rahmatullah+Hafiz%2C+and+Paul+Callaghan%2E+2007%2E+Modular+and+efficient+top%2Ddown+parsing+for+ambiguous+left%2Drecursive+grammars%2E+In+Proceedings+of+the+Tenth+International+Conference+on+Parsin)
17. Fajri Koto, Jey Han Lau, and Timothy Baldwin. 2019. Improved document modelling with a neural discourse parser. In Proceedings of the Australasian Language Technology Association Workshop 2019, Sydney, Australia. [find](https://scholar.google.com/scholar?q=Fajri+Koto%2C+Jey+Han+Lau%2C+and+Timothy+Baldwin%2E+2019%2E+Improved+document+modelling+with+a+neural+discourse+parser%2E+In+Proceedings+of+the+Australasian+Language+Technology+Association+Workshop+2019%2C+Sydney)
18. Yoav Goldberg and Joakim Nivre. 2012. A dynamic oracle for arc-eager dependency parsing. In Proceedings of COLING 2012, pages 959–976, Mumbai, India. [find](https://scholar.google.com/scholar?q=Yoav+Goldberg+and+Joakim+Nivre%2E+2012%2E+A+dynamic+oracle+for+arc%2Deager+dependency+parsing%2E+In+Proceedings+of+COLING+2012%2C+pages+959%E2%80%93976%2C+Mumbai%2C+India%2E)
19. The COLING 2012 Organizing Committee. [find](https://scholar.google.com/scholar?q=The+COLING+2012+Organizing+Committee%2E)
20. Jiwei Li, Rumeng Li, and Eduard Hovy. 2014. Recursive deep models for discourse parsing. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2061–2069, Doha, Qatar. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Jiwei+Li%2C+Rumeng+Li%2C+and+Eduard+Hovy%2E+2014%2E+Recursive+deep+models+for+discourse+parsing%2E+In+Proceedings+of+the+2014+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29%2C+pages+2061%E2%80%932) [our card](https://md.research.elamur.ai/free/en/papers/li-2014-recursive-deep-models-discourse-parsing/)
21. Katsuhiko Hayashi, Tsutomu Hirao, and Masaaki Nagata. 2016. Empirical comparison of dependency conversions for RST discourse trees. In Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 128– 136, Los Angeles. Association for Computational Linguistic… [find](https://scholar.google.com/scholar?q=Katsuhiko+Hayashi%2C+Tsutomu+Hirao%2C+and+Masaaki+Nagata%2E+2016%2E+Empirical+comparison+of+dependency+conversions+for+RST+discourse+trees%2E+In+Proceedings+of+the+17th+Annual+Meeting+of+the+Special+Interest+Gr)
22. Hugo Hernault, Helmut Prendinger, David A. duVerle, and Mitsuru Ishizuka. 2010. Hilda: A discourse parser using support vector machine classification. Dialogue and Discourse, 1(3):1–33. [find](https://scholar.google.com/scholar?q=Hugo+Hernault%2C+Helmut+Prendinger%2C+David+A%2E+duVerle%2C+and+Mitsuru+Ishizuka%2E+2010%2E+Hilda%3A+A+discourse+parser+using+support+vector+machine+classification%2E+Dialogue+and+Discourse%2C+1%283%29%3A1%E2%80%9333%2E) [our card](https://md.research.elamur.ai/free/en/papers/hernault-2010-hilda-discourse-parser-support-vector/)
23. Qi Li, Tianshi Li, and Baobao Chang. 2016. Discourse parsing with attention-based hierarchical neural networks. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 362–371, Austin, Texas. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Qi+Li%2C+Tianshi+Li%2C+and+Baobao+Chang%2E+2016%2E+Discourse+parsing+with+attention%2Dbased+hierarchical+neural+networks%2E+In+Proceedings+of+the+2016+Conference+on+Empirical+Methods+in+Natural+Language+Processin) [our card](https://md.research.elamur.ai/free/en/papers/li-2016-discourse-parsing-attention-based-hierarchical/)
24. Xiang Lin, Shafiq Joty, Prathyusha Jwalapuram, and M Saiful Bari. 2019. A unified linear-time framework for sentence-level discourse parsing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4190– 4200, Florence, Italy. Association for Computational L… [find](https://scholar.google.com/scholar?q=Xiang+Lin%2C+Shafiq+Joty%2C+Prathyusha+Jwalapuram%2C+and+M+Saiful+Bari%2E+2019%2E+A+unified+linear%2Dtime+framework+for+sentence%2Dlevel+discourse+parsing%2E+In+Proceedings+of+the+57th+Annual+Meeting+of+the+Associati) [our card](https://md.research.elamur.ai/free/en/papers/lin-2019-unified-linear-time-framework-sentence/)
25. Brian Roark. 2001. Probabilistic top-down parsing and language modeling. Computational Linguistics, 27(2):249–276. [find](https://scholar.google.com/scholar?q=Brian+Roark%2E+2001%2E+Probabilistic+top%2Ddown+parsing+and+language+modeling%2E+Computational+Linguistics%2C+27%282%29%3A249%E2%80%93276%2E)
26. Linlin Liu, Xiang Lin, Shafiq Joty, Simeng Han, and Lidong Bing. 2019. Hierarchical pointer net parsing. In 2019 Conference on Empirical Methods in Natural Language Processing, pages 1007–1017. [find](https://scholar.google.com/scholar?q=Linlin+Liu%2C+Xiang+Lin%2C+Shafiq+Joty%2C+Simeng+Han%2C+and+Lidong+Bing%2E+2019%2E+Hierarchical+pointer+net+parsing%2E+In+2019+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+1007%E2%80%931017%2E)
27. Brian Roark and Mark Johnson. 1999. Efficient probabilistic top-down and left-corner parsing. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, pages 421–428, College Park, Maryland, USA. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Brian+Roark+and+Mark+Johnson%2E+1999%2E+Efficient+probabilistic+top%2Ddown+and+left%2Dcorner+parsing%2E+In+Proceedings+of+the+37th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2C+pages+421%E2%80%93428%2C)
28. Yang Liu and Mirella Lapata. 2019. Text summarization with pretrained encoders. In 2019 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Yang+Liu+and+Mirella+Lapata%2E+2019%2E+Text+summarization+with+pretrained+encoders%2E+In+2019+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2E)
29. William C. Mann and Sandra A. Thompson. 1988. Rhetorical structure theory: Toward a functional theory of text organization. Text Interdisciplinary Journal for the Study of Discourse, pages 243–281. [find](https://scholar.google.com/scholar?q=William+C%2E+Mann+and+Sandra+A%2E+Thompson%2E+1988%2E+Rhetorical+structure+theory%3A+Toward+a+functional+theory+of+text+organization%2E+Text+Interdisciplinary+Journal+for+the+Study+of+Discourse%2C+pages+243%E2%80%93281%2E) [our card](https://md.research.elamur.ai/free/en/papers/enud-1988-mann-rhetorical-structure-theory-functional/)
30. Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014. The Stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 55–60, Baltimore, Ma… [find](https://scholar.google.com/scholar?q=Christopher+Manning%2C+Mihai+Surdeanu%2C+John+Bauer%2C+Jenny+Finkel%2C+Steven+Bethard%2C+and+David+McClosky%2E+2014%2E+The+Stanford+CoreNLP+natural+language+processing+toolkit%2E+In+Proceedings+of+52nd+Annual+Meeting) [our card](https://md.research.elamur.ai/free/en/papers/manning-2014-stanford-corenlp-natural-language-processing/)
31. Daniel Marcu. 2000. The Theory and Practice of Discourse Parsing and Summarization. MIT Press, Cambridge, USA. [find](https://scholar.google.com/scholar?q=Daniel+Marcu%2E+2000%2E+The+Theory+and+Practice+of+Discourse+Parsing+and+Summarization%2E+MIT+Press%2C+Cambridge%2C+USA%2E)
32. 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. [find](https://scholar.google.com/scholar?q=Mitchell+P%2E+Marcus%2C+Beatrice+Santorini%2C+and+Mary+Ann+Marcinkiewicz%2E+1993%2E+Building+a+large+annotated+corpus+of+English%3A+The+Penn+Treebank%2E+Computational+Linguistics%2C+19%282%29%3A313%E2%80%93330%2E) [our card](https://md.research.elamur.ai/free/en/papers/bank-1993-marcus-building-large-annotated-corpus-english/)
33. Mathieu Morey, Philippe Muller, and Nicholas Asher. 2017. How much progress have we made on RST discourse parsing? a replication study of recent results on the RST-DT. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1319–1324, Copenhagen, Denmark. Ass… [find](https://scholar.google.com/scholar?q=Mathieu+Morey%2C+Philippe+Muller%2C+and+Nicholas+Asher%2E+2017%2E+How+much+progress+have+we+made+on+RST+discourse+parsing%3F+a+replication+study+of+recent+results+on+the+RST%2DDT%2E+In+Proceedings+of+the+2017+Confe) [our card](https://md.research.elamur.ai/free/en/papers/morey-2017-how-much-progress-have-we/)
34. Allen Nie, Erin Bennett, and Noah Goodman. 2019. DisSent: Learning sentence representations from explicit discourse relations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4497–4510, Florence, Italy. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Allen+Nie%2C+Erin+Bennett%2C+and+Noah+Goodman%2E+2019%2E+DisSent%3A+Learning+sentence+representations+from+explicit+discourse+relations%2E+In+Proceedings+of+the+57th+Annual+Meeting+of+the+Association+for+Computat) [our card](https://md.research.elamur.ai/free/en/papers/nie-2019-dissent-learning-sentence-representations-explicit/)
35. Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543, Doha, Qatar. Association for Computational Linguistics. [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+Proceedings+of+the+2014+Conference+on+Empirical+Methods+in+Natural+Language+Process)
36. Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Building a discourse-tagged corpus in the framework of rhetorical structure theory. In CoRR, abs/1704.01444, 2017. [find](https://scholar.google.com/scholar?q=Alec+Radford%2C+Karthik+Narasimhan%2C+Tim+Salimans%2C+and+Ilya+Sutskever%2E+2018%2E+Building+a+discourse%2Dtagged+corpus+in+the+framework+of+rhetorical+structure+theory%2E+In+CoRR%2C+abs%2F1704%2E01444%2C+2017%2E) [our card](https://md.research.elamur.ai/free/en/papers/radford-2017-learning-generate-reviews-discovering-sentiment/)
37. Wei Shi and Vera Demberg. 2019. Next sentence prediction helps implicit discourse relation classification within and across domains. In 2019 Conference on Empirical Methods in Natural Language Processing, pages 5789–5795. [find](https://scholar.google.com/scholar?q=Wei+Shi+and+Vera+Demberg%2E+2019%2E+Next+sentence+prediction+helps+implicit+discourse+relation+classification+within+and+across+domains%2E+In+2019+Conference+on+Empirical+Methods+in+Natural+Language+Process) [our card](https://md.research.elamur.ai/free/en/papers/shi-2019-next-sentence-prediction-helps-implicit/)
38. Damien Sileo, Tim Van De Cruys, Camille Pradel, and Philippe Muller. 2019. Mining discourse markers for unsupervised sentence representation learning. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vo… [find](https://scholar.google.com/scholar?q=Damien+Sileo%2C+Tim+Van+De+Cruys%2C+Camille+Pradel%2C+and+Philippe+Muller%2E+2019%2E+Mining+discourse+markers+for+unsupervised+sentence+representation+learning%2E+In+Proceedings+of+the+2019+Conference+of+the+Nort) [our card](https://md.research.elamur.ai/free/en/papers/sileo-2019-mining-discourse-markers-unsupervised-sentence/)
39. Mihai Surdeanu, Tom Hicks, and Marco Antonio Valenzuela-Escárcega. 2015. Two practical rhetorical structure theory parsers. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pages 1–5, Denver, Colorado. Association… [find](https://scholar.google.com/scholar?q=Mihai+Surdeanu%2C+Tom+Hicks%2C+and+Marco+Antonio+Valenzuela%2DEsca%CC%81rcega%2E+2015%2E+Two+practical+rhetorical+structure+theory+parsers%2E+In+Proceedings+of+the+2015+Conference+of+the+North+American+Chapter+of+the+) [our card](https://md.research.elamur.ai/free/en/papers/surdeanu-2015-two-practical-rhetorical-structure-theory/)
40. Yizhong Wang, Sujian Li, and Houfeng Wang. 2017. A two-stage parsing method for text-level discourse analysis. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 184–188, Vancouver, Canada. Association for Computational Linguist… [find](https://scholar.google.com/scholar?q=Yizhong+Wang%2C+Sujian+Li%2C+and+Houfeng+Wang%2E+2017%2E+A+two%2Dstage+parsing+method+for+text%2Dlevel+discourse+analysis%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+for+Computational+Linguistic) [our card](https://md.research.elamur.ai/free/en/papers/wang-2017-two-stage-parsing-method-text/)
41. Nan Yu, Meishan Zhang, and Guohong Fu. 2018. Transition-based neural RST parsing with implicit syntax features. In Proceedings of the 27th International Conference on Computational Linguistics, pages 559–570. [find](https://scholar.google.com/scholar?q=Nan+Yu%2C+Meishan+Zhang%2C+and+Guohong+Fu%2E+2018%2E+Transition%2Dbased+neural+RST+parsing+with+implicit+syntax+features%2E+In+Proceedings+of+the+27th+International+Conference+on+Computational+Linguistics%2C+pages+)
42. Nan Yu, Meishan Zhang, and Guohong Fu. 2018. Transition-based neural RST parsing with implicit syntax features. In Proceedings of the 27th International Conference on Computational Linguistics, pages 559–570, Santa Fe, New Mexico, USA. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Nan+Yu%2C+Meishan+Zhang%2C+and+Guohong+Fu%2E+2018%2E+Transition%2Dbased+neural+RST+parsing+with+implicit+syntax+features%2E+In+Proceedings+of+the+27th+International+Conference+on+Computational+Linguistics%2C+pages+)
43. Longyin Zhang, Yuqing Xing, Fang Kong, Peifeng Li, and Guodong Zhou. 2020. A top-down neural architecture towards text-level parsing of discourse rhetorical structure. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6386–6395, Online. Association for… [find](https://scholar.google.com/scholar?q=Longyin+Zhang%2C+Yuqing+Xing%2C+Fang+Kong%2C+Peifeng+Li%2C+and+Guodong+Zhou%2E+2020%2E+A+top%2Ddown+neural+architecture+towards+text%2Dlevel+parsing+of+discourse+rhetorical+structure%2E+In+Proceedings+of+the+58th+Annua) [our card](https://md.research.elamur.ai/free/en/papers/zhang-2020-top-down-neural-architecture-text/)