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eRST: A Signaled Graph Theory of Discourse Relations and Organization

What it is about

Enhanced RST keeps the RST tree (projective, nucleus/satellite, one label per edge, over elementary discourse units) and adds two things. Secondary edges may break the tree (non-projective, cyclic, a second relation between already linked units), but only when licensed by a clear signal: an «orphan» discourse marker with no matching primary relation, or an unambiguous syntactic environment such as reported speech or cognition. Signals — the evidence for each relation — are anchored to tokens and typed after the RST Signalling Corpus: discourse markers plus graphical, lexical, morphological, numerical, reference, semantic and syntactic signals (e.g. a question mark for topic-question, a pronoun for elaboration, an antonym for contrast). The framework is applied to all of GUM (12 genres, 213 documents, 26K EDUs, later 24 genres) with 32 relation labels in classes (adversative, attribution, causal, context, contingency, elaboration, evaluation, explanation, joint, mode, organization, purpose, restatement, topic). Secondary edges matter most for causal-result (14%), explanation-justify (12%) and concession (10%); 62% of evaluations have an indicative word (good, very, feel, great, important …). A parsing baseline is given; secondary edges are hard to predict.

Ideas

For Mova

36. Discourse relations and coherence §3 (RST), §10, §11.1, §11.6; 30-pragmatics-overview.md (explanations). Code: prag/src/frames.rs (links with signals), a GUM eRST scorer next to prag/src/fbench.rs, prag/data/cues.tsv. Measures: link precision/recall vs GUM eRST on dev; frame bench F1 by signal type.

Topics: Discourse analysis

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00538 · arXiv 2403.13560

@article{zeldes2025erst,
  title = {{eRST: A Signaled Graph Theory of Discourse Relations and Organization}},
  author = {Zeldes, Amir and Aoyama, Tatsuya and Liu, Yang Janet and Peng, Siyao and Das, Debopam and Gessler, Luke},
  journal = {Computational Linguistics},
  year = {2025},
  doi = {10.1162/coli\_a\_00538},
  eprint = {2403.13560},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1162/coli_a_00538}
}

References

110 references, 21 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Afantenos, Stergos, Pascal Denis, Philippe Muller, and Laurence Danlos. 2010. Learning recursive segments for discourse parsing. In Proceedings of LREC 2010, pages 3578–3584. find
  2. Anuranjana, Kaveri. 2023. DiscoFlan: Instruction fine-tuning and refined text generation for discourse relation label classification. In Proceedings of the 3rd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2023), pages 22–28. https://doi.org/10.18653/v1/2023.disrpt-1.2 ↗
  3. Aoyama, Tatsuya, Shabnam Behzad, Luke Gessler, Lauren Levine, Jessica Lin, Yang Janet Liu, Siyao Peng, Yilun Zhu, and Amir Zeldes. 2023. GENTLE: A genre-diverse multilayer challenge set for English NLP and linguistic evaluation. In Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII), pages 166–178. https://doi.org/10.18653/v1/2023.law-1.17 ↗ our card
  4. Asher, Nicholas, Julie Hunter, Mathieu Morey, Benamara Farah, and Stergos Afantenos. 2016. Discourse structure and dialogue acts in multiparty dialogue: The STAC corpus. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 2721–2727. find our card
  5. Asher, Nicholas and Alex Lascarides. 2003. Logics of Conversation, Studies in Natural Language Processing. Cambridge University Press. find
  6. Asher, Nicholas and Laure Vieu. 2005. Subordinating and coordinating discourse relations. Lingua, 115:591–610. https://doi.org/10.1016/j.lingua.2003.09.017 ↗
  7. Atutxa, Aitziber, Kepa Bengoetxea, Arantza Diaz de Ilarraza, and Mikel Iruskieta. 2019. Towards a top-down approach for an automatic discourse analysis for Basque: Segmentation and central unit detection tool. PLOS ONE, 14(9):1–25. https://doi.org/10.1371/journal.pone.0221639 PubMed: 31483814 ↗
  8. Biber, Douglas and Bethany Gray. 2010. Challenging stereotypes about academic writing: Complexity, elaboration, explicitness. Journal of English for Academic Purposes, 9(1):2–20. https://doi.org/10.1016/j.jeap.2010.01.001 ↗
  9. Black, E., S. Abney, D. Flickenger, C. Gdaniec, R. Grishman, P. Harrison, D. Hindle, R. Ingria, F. Jelinek, J. Klavans, M. Liberman, M. Marcus, S. Roukos, B. Santorini, and T. Strzalkowski. 1991. A procedure for quantitatively comparing the syntactic coverage of English grammars. In Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, pages 308–311. find
  10. Bourgonje, Peter and Manfred Stede. 2020. The Potsdam commentary corpus 2.2: Extending annotations for shallow discourse parsing. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1061–1066. find
  11. Brants, Sabine, Stefanie Dipper, Silvia Hansen, Wolfgang Lezius, and George Smith. 2002. The TIGER Treebank. In Proceedings of the Workshop on Treebanks and Linguistic Theories, pages 24–42. find
  12. Braud, Chloé, Yang Janet Liu, Eleni Metheniti, Philippe Muller, Laura Rivière, Attapol Rutherford, and Amir Zeldes. 2023. find
  13. The DISRPT 2023 shared task on elementary discourse unit segmentation, connective detection, and relation classification. In Proceedings of the 3rd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2023), pages 1–21. https://doi.org/10.18653/v1/2023.disrpt-1.1 ↗ our card
  14. Braud, Chloé, Barbara Plank, and Anders Søgaard. 2016. Multi-view and multi-task training of RST discourse parsers. In Proceedings of COLING 2016, pages 1903–1913. find
  15. Braud, Chloé, Amir Zeldes, Laura Rivière, Yang Janet Liu, Philippe Muller, Damien Sileo, and Tatsuya Aoyama. 2024. DISRPT: A multilingual, multi-domain, cross-framework benchmark for discourse processing. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 4990–5005. find
  16. Carlson, Lynn, Daniel Marcu, and Mary Ellen Okurowski. 2001. Building a discourse-tagged corpus in the framework of Rhetorical Structure Theory. In Proceedings of 2nd SIGDIAL Workshop on Discourse and Dialogue, Eurospeech 2001, pages 1–10. https://doi.org/10 .21236/ADA460581 ↗ our card
  17. Chan, Chunkit, Xin Liu, Jiayang Cheng, Zihan Li, Yangqiu Song, Ginny Wong, and Simon See. 2023. DiscoPrompt: Path prediction prompt tuning for implicit discourse relation recognition. In Findings of the Association for Computational Linguistics: ACL 2023, pages 35–57. https://doi.org/10.18653/v1/2023.findings-acl.4 ↗
  18. Cheng, Yi and Sujian Li. 2019. Zero-shot Chinese discourse dependency parsing via cross-lingual mapping. In Proceedings of the 1st Workshop on Discourse Structure in Neural NLG, pages 24–29. https://doi.org/10.18653/v1/W19-8104 ↗
  19. Crible, Ludivine. 2022. The syntax and semantics of coherence relations: From relative configurations to predictive signals. International Journal of Corpus Linguistics, 27(1):59–92. https://doi.org/10.1075/ijcl.19109.cri Dai, Zeyu 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. https://doi.org/10.18653/v1/N18-1013 ↗
  20. Das, Debopam and Maite Taboada. 2014. RST Signalling Corpus Annotation Manual. Unpublished manuscript. find
  21. Das, Debopam and Maite Taboada. 2017. Signalling of coherence relations in discourse, beyond discourse markers. Discourse Processes, 55(8):743–770. https://doi.org/10.1080/0163853X.2017.1379327 ↗
  22. Das, Debopam and Maite Taboada. 2018. RST Signalling Corpus: A corpus of signals of coherence relations. Language Resources and Evaluation, 52(1):149–184. https://doi.org/10.1007/s10579-017-9383-x ↗
  23. de Marneffe, Marie Catherine, Christopher D. Manning, Joakim Nivre, and Daniel Zeman. 2021. Universal Dependencies. Computational Linguistics, 47(2):255–308. https://doi.org/10 .1162/coli_a_00402 ↗ our card
  24. Demberg, Vera, Fatemeh Torabi Asr, and Merel Scholman. 2019. How compatible are our discourse annotation frameworks? Insights from mapping RST-DT and PDTB annotations. Dialogue & Discourse, 10(1):87–135. https://doi.org/10.5087/dad.2019.104 ↗
  25. Feng, Vanessa Wei 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. https://doi.org/10.3115/v1/P14-1048 Gessler, Luke, Shabnam Behzad, Yang Janet Liu, Siyao Peng, Yilun Zhu, and Amir Zeldes. 2021. DisCoDisCo at the DISRPT2021 shared task: A system for discourse segmentation, classification, and connective detection. In Proceedings of Discourse Relation Parsing and Treebanking 2021 (DISRPT 2021), pages 51–62. https://doi.org/10.18653/v1/2021.disrpt-1.6 ↗
  26. Gessler, Luke, Yang Liu, and Amir Zeldes. 2019. A discourse signal annotation system for RST trees. In Proceedings of Discourse Relation Treebanking and Parsing (DISRPT 2019), pages 56–61. https://doi.org/10.18653/v1/W19-2708 ↗ our card
  27. Gessler, Luke, Siyao Peng, Yang Liu, Yilun Zhu, Shabnam Behzad, and Amir Zeldes. 2020. AMALGUM – A free, balanced, multilayer English Web corpus. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5267–5275. find our card
  28. Guz, Grigorii and Giuseppe Carenini. 2020. Coreference for discourse parsing: A neural approach. In Proceedings of the First Workshop on Computational Approaches to Discourse, pages 160–167. https://doi.org/10.18653/v1/2020.codi -1.17 ↗
  29. Guz, Grigorii, Patrick Huber, and Giuseppe Carenini. 2020. Unleashing the power of neural discourse parsers—A context and structure aware approach using large scale pretraining. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3794–3805. https://doi.org/10.18653/v1/2020.coling -main.337 ↗
  30. Hernault, Hugo, 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. https://doi.org/10.5087/dad .2010.003 ↗
  31. Hirao, Tsutomu, Yasuhisa Yoshida, Masaaki Nishino, Norihito Yasuda, and Masaaki Nagata. 2013. Single-document summarization as a tree knapsack problem. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1515–1520. find
  32. Hoek, Jet, Jacqueline Evers-Vermeul, and Ted J. M. Sanders. 2019. Using the cognitive approach to coherence relations for discourse annotation. Dialogue and Discourse, 10(2):1–33. https://doi.org/10.5087/dad.2019.201 ↗
  33. Hovy, Eduard H. 1990. Parsimonious and profligate approaches to the question of discourse structure relations. In Proceedings of the Fifth International Workshop on Natural Language Generation, pages 128–136. find
  34. Huber, Patrick and Giuseppe Carenini. 2022. Towards understanding large-scale discourse structures in pre-trained and fine-tuned language models. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2376–2394. https://doi.org/10.18653/v1/2022.naacl-main.170 ↗
  35. Hughes, Rebecca. 1996. English in Speech and Writing. Routledge. find
  36. Jurafsky, Daniel and James H. Martin. 2024. Speech and Language Processing. An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 3rd edition. find
  37. Kamp, Hans, Josef Van Genabith, and Uwe Reyle. 2011. Discourse representation theory. Handbook of Philosophical Logic: Volume 15, pages 125–394. https://doi.org/10.1007/978-94-007-0485-5_3 ↗
  38. Kim, Najoung, 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. https://doi.org/10.18653/v1/2020.acl-main.480 ↗ our card
  39. Kleijn, Suzanne, Henk L. W. Pander Maat, and Ted J. M. Sanders. 2019. Comprehension effects of connectives across texts, readers, and coherence relations. Discourse Processes, 56(5–6):447–464. https://doi.org/10.1080/0163853X.2019.1605257 ↗
  40. Knaebel, René. 2021. discopy: A neural system for shallow discourse parsing. In Proceedings of the 2nd Workshop on Computational Approaches to Discourse, pages 128–133. https://doi.org/10.18653/v1/2021.codi-main.12 ↗
  41. Kobayashi, Naoki, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, and Masaaki Nagata. 2020. Top-down RST parsing utilizing granularity levels in documents. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05):8099–8106. https://doi.org/10.1609/aaai.v34i05.6321 ↗
  42. Kobayashi, Naoki, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, and Masaaki Nagata. 2022. A simple and strong baseline for end-to-end neural RST-style discourse parsing. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6725–6737. https://doi.org/10.18653/v1/2022 .findings-emnlp.501 ↗
  43. Krause, Thomas, Ulf Leser, and Anke Lüdeling. 2016. graphANNIS: A fast query engine for deeply annotated linguistic corpora. Journal for Language Technology and Computational Linguistics, 31(1):1–25. https://doi.org/10.21248/jlcl.31.2016.199 ↗
  44. Krause, Thomas and Amir Zeldes. 2016. ANNIS3: A new architecture for generic corpus query and visualization. Digital Scholarship in the Humanities, 31(1):118–139. https://doi.org/10.1093/llc/fqu057 ↗
  45. Kurfalı, Murathan and Robert Östling. 2021. Probing multilingual language models for discourse. In Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pages 8–19. https://doi.org/10.18653/v1/2021.repl4nlp-1.2 ↗
  46. Lascarides, Alex and Nicholas Asher. 2007. Segmented Discourse Representation Theory: Dynamic semantics with discourse structure. In Computing Meaning, Studies in Linguistics and Philosophy 3. Springer, pages 87–124. https://doi.org/10.1007/978-1-4020-5958-2_5 ↗
  47. Li, Jiaqi, Ming Liu, Min-Yen Kan, Zihao Zheng, Zekun Wang, Wenqiang Lei, Ting Liu, and Bing Qin. 2020. Molweni: A challenge multiparty dialogues-based machine reading comprehension dataset with discourse structure. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2642–2652. https://doi.org/10.18653/v1/2020.coling-main.238 ↗
  48. Li, Sujian, Liang Wang, Ziqiang Cao, and Wenjie Li. 2014. Text-level discourse dependency parsing. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 25–35. https://doi.org/10.3115/v1/P14-1003 ↗
  49. Liu, Wei, Yi Fan, and Michael Strube. 2023. HITS at DISRPT 2023: Discourse segmentation, connective detection, and relation classification. In Proceedings of the 3rd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2023), pages 43–49. https://doi.org/10.18653/v1/2023.disrpt-1.4 ↗
  50. Liu, Yang. 2019. Beyond the Wall Street Journal: Anchoring and comparing discourse signals across genres. In Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019, pages 72–81. https://doi.org/10 .18653/v1/W19-2710 ↗ our card
  51. Liu, Yang. 2024. Generalizability and Genre Effects in Discourse Understanding and Parsing in Rhetorical Structure Theory. Ph.D. thesis, Georgetown University. find
  52. Liu, Yang and Amir Zeldes. 2019. Discourse relations and signaling information: Anchoring discourse signals in RST-DT. Proceedings of the Society for Computation in Linguistics, 2(35):314–317. find
  53. Liu, Yang Janet, Tatsuya Aoyama, and Amir Zeldes. 2023. What’s hard in English RST parsing? Predictive models for error analysis. In Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue, pages 31–42. https://doi.org/10.18653/v1/2023.sigdial-1.3 ↗
  54. Liu, Zhengyuan, Ke Shi, and Nancy Chen. 2021. DMRST: A joint framework for document-level multilingual RST discourse segmentation and parsing. In Proceedings of the 2nd Workshop on Computational Approaches to Discourse, pages 154–164. https://doi.org/10.18653/v1/2021.codi-main.15 ↗
  55. Mann, William C. 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
  56. Marcu, Daniel. 1997. The Rhetorical Parsing, Summarization, and Generation of Natural Language Texts. Ph.D. thesis, University of Toronto. find
  57. Marcus, Mitchell P., Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993. Building a large annotated corpus of English: The Penn Treebank. Special Issue on Using Large Corpora, Computational Linguistics, 19(2):313–330. https://doi.org/10 .21236/ADA273556 ↗ our card
  58. Mendes, Amália and Pierre Lejeune. 2022. CRPC-DB a discourse bank for Portuguese. In Computational Processing of the Portuguese Language: 15th International Conference, PROPOR 2022, Proceedings, pages 79–89. https://doi.org/10.1007/978-3-030-98305-5_8 ↗
  59. Metheniti, Eleni, Chloé Braud, Philippe Muller, and Laura Rivière. 2023. DisCut and DiscReT: MELODI at DISRPT 2023. In Proceedings of the 3rd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2023), pages 29–42. https://doi.org/10.18653/v1/2023 .disrpt-1.3 ↗
  60. Mikulová, Marie, Milan Straka, Jan Štěpánek, Barbora Štěpánková, and Jan Hajic. 2022. Quality and efficiency of manual annotation: Pre-annotation bias. In Proceedings of the Language Resources and Evaluation Conference, pages 2909–2918. find
  61. Moore, Johanna D. and Cecile L. Paris. 1993. Planning text for advisory dialogues: Capturing intentional and rhetorical information. Computational Linguistics, 19(4):651–694. find
  62. Moore, Johanna D. and Martha E. Pollack. 1992. A problem for RST: The need for multi-level discourse analysis. Computational Linguistics, 18(4):537–544. find
  63. Morey, Mathieu, 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. https://doi.org/10.18653/v1/D17 -1136 ↗ our card
  64. Morey, Mathieu, Philippe Muller, and Nicholas Asher. 2018. A dependency perspective on RST discourse parsing and evaluation. Computational Linguistics, 44(2):197–235. https://doi.org/10 .1162/COLI_a_00314 ↗ our card
  65. Nishida, Noriki and Yuji Matsumoto. 2022. Out-of-domain discourse dependency parsing via bootstrapping: An empirical analysis on its effectiveness and limitation. Transactions of the Association for Computational Linguistics, 10:127–144. https://doi.org/10.1162/tacl _a_00451 ↗
  66. Nivre, Joakim, Marie-Catherine de Marneffe, Filip Ginter, Jan Hajič, Christopher D. Manning, Sampo Pyysalo, Sebastian Schuster, Francis Tyers, and Daniel Zeman. 2020. Universal Dependencies v2: An evergrowing multilingual treebank collection. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4034–4043. find our card
  67. Peng, Siyao. 2023. Cross-Paragraph Discourse Structure in Rhetorical Structure Theory Parsing and Treebanking for Chinese and English. Ph.D. thesis, Georgetown University. find
  68. Peng, Siyao, Yang Janet Liu, and Amir Zeldes. 2022. GCDT: A Chinese RST treebank for multigenre and multilingual discourse parsing. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 382–391. find
  69. Peng, Siyao and Amir Zeldes. 2018. All roads lead to UD: Converting Stanford and Penn parses to English Universal Dependencies with multilayer annotations. In Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018), pages 167–177. find our card
  70. Porter, Martin F. 2001. Snowball: A language for stemming algorithms. http://snowball.tartarus.org/texts/introduction Potter, Andrew. 2019. The rhetorical structure of attribution. In Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019, pages 38–49. https://doi.org/10.18653/v1/W19-2706 ↗
  71. Prasad, Rashmi, Eleni Miltsakaki, Nikhil Dinesh, Alan Lee, Aravind Joshi, and Bonnie Webber. 2006. The Penn Discourse TreeBank 1.0 annotation manual. Technical report, University of Pennsylvania, PDTB Research Group. find our card
  72. Prasad, Rashmi, Bonnie Webber, and Aravind Joshi. 2014. Reflections on the Penn Discourse TreeBank, comparable corpora, and complementary annotation. Computational Linguistics, 40(4):921–950. https://doi.org/10.1162/COLI _a_00204 ↗ our card
  73. Pu, Dongqi, Yifan Wang, and Vera Demberg. 2023. Incorporating distributions of discourse structure for long document abstractive summarization. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5574–5590. https://doi.org/10.18653/v1/2023.acl-long.306 ↗
  74. Qin, Lianhui, Zhisong Zhang, Hai Zhao, Zhiting Hu, and Eric Xing. 2017. Adversarial connective-exploiting networks for implicit discourse relation classification. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1006–1017. https://doi.org/10.18653/v1/P17-1093 ↗
  75. Rehbein, Ines, Merel Scholman, and Vera Demberg. 2016. Annotating discourse relations in spoken language: A comparison of the PDTB and CCR frameworks. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 1039–1046. find
  76. Rutherford, Attapol, Vera Demberg, and Nianwen Xue. 2017. A systematic study of neural discourse models for implicit discourse relation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 281–291. https://doi.org/10.18653/v1/E17-1027 ↗
  77. Sanders, Ted J. M., Vera Demberg, Jet Hoek, C. J. Scholman, Merel, Fatemeh Torabi Asr, Sandrine Zufferey, and Jacqueline Evers-Vermeul. 2021. Unifying dimensions in coherence relations: How various annotation frameworks are related. Corpus Linguistics and Linguistic Theory, 17(1):1–71. https://doi.org/10.1515/cllt -2016-0078 ↗
  78. Sanders, Ted J. M., Jentine Land, and Gerben Mulder. 2007. Linguistic markers of coherence improve text comprehension in functional contexts. Information Design Journal, 15(3):219–235. https://doi.org/10.1075/idj.15.3.04san Sanders, Ted J. M., Wilbert P. M. Spooren, and Leo G. M. Noordman. 1992. Towards a taxonomy of coherence relations. Discourse Processes, 15:1–35. https://doi.org/10.1080/01638539209544800 ↗
  79. Sanguinetti, Manuela, Cristina Bosco, Lauren Cassidy, Özlem Çetinoğlu, Alessandra Teresa Cignarella, Teresa Lynn, Ines Rehbein, Josef Ruppenhofer, Djamé Seddah, and Amir Zeldes. 2020. Treebanking user-generated content: A proposal for a unified representation in Universal Dependencies. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5240–5250. find
  80. Sanguinetti, Manuela, Lauren Cassidy, Cristina Bosco, Özlem Çetinoğlu, Alessandra Teresa Cignarella, Teresa Lynn, Ines Rehbein, Josef Ruppenhofer, Djamé Seddah, and Amir Zeldes. 2022. Treebanking user-generated content: A UD based overview of guidelines, corpora and unified recommendations. Language Resources and Evaluation, 57:493–544. https://doi.org/10.1007/s10579 -022-09581-9 ↗
  81. Scholman, Merel, Tianai Dong, Frances Yung, and Vera Demberg. 2021. Comparison of methods for explicit discourse connective identification across various domains. In Proceedings of the 2nd Workshop on Computational Approaches to Discourse, pages 95–106. https://doi.org/10.18653/v1/2021.codi -main.9 ↗
  82. Shen, Andrew, Fajri Koto, Jey Han Lau, and Timothy Baldwin. 2022. Easy-first bottom-up discourse parsing via sequence labelling. In Proceedings of the 3rd Workshop on Computational Approaches to Discourse, pages 35–41. find
  83. Speer, Robyn, Joshua Chin, and Catherine Havasi. 2017. ConceptNet 5.5: An open multilingual graph of general knowledge. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pages 4444–4451. AAAI Press. https://doi.org/10.1609/aaai.v31i1.11164 ↗
  84. Stede, Manfred. 2008. RST revisited: Disentangling nuclearity. In Cathrine Fabricius-Hansen and Wiebke Ramm, editors, ’Subordination’ versus ’Coordination’ in Sentence and Text: A cross-linguistic perspective, Studies in Language Companion Series 98. John Benjamins, Amsterdam, pages 33–58. https://doi.org/10.1075/slcs.98.03ste ↗
  85. Stede, Manfred. 2012. Discourse Processing, Synthesis Lectures on Human Language Technologies 4. Morgan & Claypool, [San Rafael, CA]. https://doi.org/10.1007/978-3-031-02144-2 ↗
  86. Stede, Manfred, 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. find
  87. Stede, Manfred and Arne Neumann. 2014. Potsdam Commentary Corpus 2.0: Annotation for discourse research. In Proceedings of the Language Resources and Evaluation Conference (LREC ’14), pages 925–929. find
  88. Sun, Kun and Rong Wang. 2022. Constructing the corpus of Chinese textual ’run-on’ sentences (CCTRS): Discourse corpus benchmark with multi-layer annotations. In International Conference on Natural Language and Speech Processing (ICNLSP)-2022, Trento, Italy. https://doi.org/10.31234/osf.io/jua9g ↗
  89. Taboada, Maite and Julia Lavid. 2003. Rhetorical and thematic patterns in scheduling dialogues: A generic characterization. Functions of Language, 10(2):147–179. https://doi.org/10.1075/fol.10.2.02tab Taboada, Maite and William C. Mann. 2006. Rhetorical Structure Theory: Looking back and moving ahead. Discourse Studies, 8:423–459. https://doi.org/10.1177/1461445606061881 ↗
  90. Teufel, Simone and Marc Moens. 2002. Summarising scientific articles—Experiments with relevance and rhetorical status. Computational Linguistics, 28(4):409–445. https://doi.org/10.1162/089120102762671936 ↗
  91. Tonelli, Sara, Giuseppe Riccardi, Rashmi Prasad, and Aravind Joshi. 2010. Annotation of discourse relations for conversational spoken dialogs. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10). find
  92. Vis, Kirsten, José Sanders, and Wilbert Spooren. 2012. Diachronic changes in subjectivity and stance—A corpus linguistic study of Dutch news texts. Discourse, Context & Media, 1(2):95–102. https://doi.org/10.1016/j.dcm .2012.09.003 ↗
  93. Webber, Bonnie. 2013. What excludes an alternative in coherence relations? In Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers, pages 276–287. find
  94. Widlöcher, Antoine and Yann Mathet. 2012. The glozz platform: A corpus annotation and mining tool. In Proceedings of the 2012 ACM symposium on document engineering, pages 171–180. https://doi.org/10.1145/2361354.2361394 ↗
  95. Xing, Linzi, Patrick Huber, and Giuseppe Carenini. 2022. Improving topic segmentation by injecting discourse dependencies. In Proceedings of the 3rd Workshop on Computational Approaches to Discourse, pages 7–18. find
  96. Xue, Nianwen, 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. https://doi.org/10 .18653/v1/K16-2001 ↗ our card
  97. Yang, An and Sujian Li. 2018. SciDTB: Discourse dependency TreeBank for scientific abstracts. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 444–449. https://doi.org/10.18653/v1/P18-2071 ↗ our card
  98. Yi, Cheng, Li Sujian, and Li Yueyuan. 2021. Unifying discourse resources with dependency framework. In Proceedings of the 20th Chinese National Conference on Computational Linguistics, pages 1058–1065. https://doi.org/10.1007/978-3 -030-84186-7_17 ↗
  99. Yu, Nan, Meishan Zhang, Guohong Fu, and Min Zhang. 2022. RST discourse parsing with second-stage EDU-level pre-training. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4269–4280. https://doi.org/10.18653/v1/2022.acl-long.294 ↗
  100. Yu, Yue, Yilun Zhu, Yang Liu, Yan Liu, Siyao Peng, Mackenzie Gong, and Amir Zeldes. 2019. GumDrop at the DISRPT2019 shared task: A model stacking approach to discourse unit segmentation and connective detection. In Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019, pages 133–143. https://doi.org/10.18653/v1/W19-2717 ↗ our card
  101. Zeldes, Amir. 2016. rstWeb—A browser-based annotation interface for Rhetorical Structure Theory and discourse relations. In Proceedings of NAACL-HLT 2016 System Demonstrations, pages 1–5. https://doi.org/10.18653/v1/N16-3001 ↗
  102. Zeldes, Amir. 2017. The GUM corpus: Creating multilayer resources in the classroom. Language Resources and Evaluation, 51(3):581–612. https://doi.org/10.1007/s10579-016-9343-x ↗
  103. Zeldes, Amir. 2022. Can we fix the scope for coreference? Problems and solutions for benchmarks beyond OntoNotes. Dialogue & Discourse, 13(1):41–62. https://doi.org/10.5210/dad.2022.102 ↗ our card
  104. Zeldes, Amir, Yang Janet Liu, Mikel Iruskieta, Philippe Muller, Chloé Braud, and Sonia Badene. 2021. find
  105. The DISRPT 2021 shared task on elementary discourse unit segmentation, connective detection, and relation classification. In Proceedings of Discourse Relation Parsing and Treebanking 2021 (DISRPT 2021), pages 1–12. https://doi.org/10.18653/v1/2021.disrpt-1.1 ↗
  106. Zeyrek, Deniz and Murathan Kurfalı. 2017. TDB 1.1: Extensions on Turkish discourse bank. In Proceedings of the 11th Linguistic Annotation Workshop, pages 76–81. https://doi.org/10.18653/v1/W17 -0809 ↗
  107. Zeyrek, Deniz, Amalia Mendes, Yulia Grishina, Murathan Kurfali, Samuel Gibbon, and Maciej Ogrodniczuk. 2019. TED multilingual discourse bank (TED-MDB): A parallel corpus annotated in the PDTB style. Language Resources and Evaluation, pages 1–38. https://doi.org/10.1007/s10579-019-09445-9 ↗
  108. Zeyrek, Deniz, Amália Mendes, and Murathan Kurfalı. 2018. Multilingual extension of PDTB-style annotation: The case of TED multilingual discourse bank. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). find
  109. Zhang, Longyin, Fang Kong, and Guodong Zhou. 2021. Adversarial learning for discourse rhetorical structure parsing. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3946–3957. https://doi.org/10.18653/v1/2021.acl-long.305 ↗
  110. Zhou, Yuping, Jill Lu, Jennifer Zhang, and Nianwen Xue. 2014. Chinese Discourse Treebank 0.5 LDC2014T21. find

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