--- title: "A Refined End-to-End Discourse Parser — Wang 2015" description: "This paper describes the system that East China Normal University submitted to the shallow discourse parsing competition held as the CoNLL-2015 shared…" authors: ["Jianxiang Wang", "Man Lan"] year: 2015 venue: "Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task" source: "https://aclanthology.org/K15-2002/" doi: "10.18653/v1/k15-2002" licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/papers/wang-2015-refined-end-end-discourse-parser/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/wang-2015-refined-end-end-discourse-parser/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2015](https://md.research.elamur.ai/free/papers/2015/) / A Refined End-to-End Discourse Parser # A Refined End-to-End Discourse Parser **Authors:** [Jianxiang Wang](https://md.research.elamur.ai/free/authors/jianxiang-wang/), [Man Lan](https://md.research.elamur.ai/free/authors/man-lan/) · **Year:** 2015 · **Venue:** Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/) **Links:** [Publisher's page](https://aclanthology.org/K15-2002/) · [PDF](https://aclanthology.org/K15-2002.pdf) · [DOI 10.18653/v1/k15-2002](https://doi.org/10.18653/v1/k15-2002) · [OpenAlex](https://openalex.org/W2250424758) **Citations:** 32 citations (openalex-snapshot · 2026-10-14) · [49](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary This paper describes the system that East China Normal University submitted to the shallow discourse parsing competition held as the CoNLL-2015 shared task. The task takes raw newswire text and outputs PDTB-style discourse relations: connectives, their two argument spans and sense labels. The authors start from the sequential pipeline of Lin et al. (2014) and add several improvements. Their parser has nine components, split into an Explicit part and a Non-Explicit part. For Explicit relations whose Arg1 sits in an earlier sentence (PS), they train separate clause-level extractors for Arg1 and Arg2 instead of one shared model. Non-Explicit relations that are not EntRel get their own dedicated Arg1/Arg2 extractors. Sense classification is then run on these refined spans. The system also adds new syntactic and connective-related features to existing feature sets, and it ranked first on both official test sets. This matters because argument span extraction and Non-Explicit sense labeling are the hardest parts of end-to-end discourse parsing. ## Key points - The pipeline has 9 components: connective classifier, Arg1 position classifier (SS vs. PS), SS argument extractor following Kong et al. (2014), PS Arg1 and PS Arg2 extractors, Explicit sense classifier, Non-Explicit sense classifier, and Implicit Arg1 and Arg2 extractors. - On PDTB Section 23, the overall parser F1 is about 29.7%, and Explicit connective identification reaches about 94.2% F1. The new features improved connective classification by 0.54 F1 points over Lin's parser. - On the blind test set, overall F1 is about 24.0%, ahead of the second-ranked system (about 21.8%). The largest margins are in argument extraction. - During development, running Non-Explicit sense classification on refined arguments instead of whole adjacent sentences raised overall F1 by 0.41 points (37.43 → 37.84). - The two test sets differ in which connectives are annotated. For example, "upon" counts as a connective in the blind test data but not in PDTB. *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/shelves/linguistics/) Topics: [Discourse analysis](https://md.research.elamur.ai/free/topics/discourse-analysis/) ## Cited by Cited by 49 papers of our library cite it (of the 140673 whose bibliographies we read); 19 of them have a page here, 30 not yet. - [Where Are We in Discourse Relation Recognition?](https://md.research.elamur.ai/free/papers/atwell-2021-where-are-we-discourse-relation/) - [Identifying Explicit Discourse Connectives in German](https://md.research.elamur.ai/free/papers/bourgonje-2018-identifying-explicit-discourse-connectives-german/) - [The Potsdam Commentary Corpus 2.2: Extending Annotations for Shallow Discourse Parsing](https://md.research.elamur.ai/free/papers/bourgonje-2020-potsdam-commentary-corpus-2-2/) - [Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing](https://md.research.elamur.ai/free/papers/knaebel-2020-contextualized-embeddings-connective-disambiguation-shallow/) - [Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings](https://md.research.elamur.ai/free/papers/mihaylov-2016-discourse-relation-sense-classification-cross/) - [OPT: Oslo–Potsdam–Teesside. Pipelining Rules, Rankers, and Classifier Ensembles for Shallow Discourse Parsing](https://md.research.elamur.ai/free/papers/oepen-2016-opt-oslo-potsdam-teesside-pipelining/) - [Adapting Event Embedding for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/pacheco-2016-adapting-event-embedding-implicit-discourse/) - [Towards Full Text Shallow Discourse Relation Annotation: Experiments with Cross-Paragraph Implicit Relations in the PDTB](https://md.research.elamur.ai/free/papers/prasad-2017-full-text-shallow-discourse-relation/) - [Shallow Discourse Parsing Using Convolutional Neural Network](https://md.research.elamur.ai/free/papers/qin-2016-shallow-discourse-parsing-convolutional-neural/) - [Robust Non-Explicit Neural Discourse Parser in English and Chinese](https://md.research.elamur.ai/free/papers/rutherford-2016-robust-non-explicit-neural-discourse/) - [A Systematic Study of Neural Discourse Models for Implicit Discourse Relation](https://md.research.elamur.ai/free/papers/rutherford-2017-systematic-study-neural-discourse-models/) - [Comparison of methods for explicit discourse connective identification across various domains](https://md.research.elamur.ai/free/papers/scholman-2021-comparison-methods-explicit-discourse-connective/) - [UniTN End-to-End Discourse Parser for CoNLL 2016 Shared Task](https://md.research.elamur.ai/free/papers/stepanov-2016-unitn-end-end-discourse-parser/) - [Two End-to-end Shallow Discourse Parsers for English and Chinese in CoNLL-2016 Shared Task](https://md.research.elamur.ai/free/papers/wang-2016-two-end-end-shallow-discourse/) - [The CoNLL-2015 Shared Task on Shallow Discourse Parsing](https://md.research.elamur.ai/free/papers/xue-2015-conll-2015-shared-task-shallow/) - [CoNLL 2016 Shared Task on Multilingual Shallow Discourse Parsing](https://md.research.elamur.ai/free/papers/xue-2016-conll-2016-shared-task-multilingual/) - [Modelling the Usage of Discourse Connectives as Rational Speech Acts](https://md.research.elamur.ai/free/papers/yung-2016-modelling-usage-discourse-connectives-as/) - [The DISRPT 2021 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification](https://md.research.elamur.ai/free/papers/zeldes-2021-disrpt-2021-shared-task-elementary/) - [Revisiting Shallow Discourse Parsing in the PDTB-3: Handling Intra-sentential Implicits](https://md.research.elamur.ai/free/papers/zhao-2021-revisiting-shallow-discourse-parsing-pdtb/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{wang2015refined, title = {{A Refined End-to-End Discourse Parser}}, author = {Wang, Jianxiang and Lan, Man}, booktitle = {Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task}, year = {2015}, doi = {10.18653/v1/k15-2002}, url = {https://doi.org/10.18653/v1/k15-2002} } ``` - [← previous A Neural Network Approach to Context-Sensitive Generation of Conversational Responses](https://md.research.elamur.ai/free/papers/sordoni-2015-neural-network-approach-context-sensitive/) - [next → A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](https://md.research.elamur.ai/free/papers/gal-2015-theoretically-grounded-application-dropout-recurrent/) - [↑ back to papers of 2015](https://md.research.elamur.ai/free/papers/2015/) ## References 11 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Sucheta Ghosh, Richard Johansson, and Sara Tonelli. 2011. Shallow discourse parsing with conditional random fields. In In Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP 2011. Citeseer. [find](https://scholar.google.com/scholar?q=Sucheta+Ghosh%2C+Richard+Johansson%2C+and+Sara+Tonelli%2E+2011%2E+Shallow+discourse+parsing+with+conditional+random+fields%2E+In+In+Proceedings+of+the+5th+International+Joint+Conference+on+Natural+Language+Proc) [our card](https://md.research.elamur.ai/free/papers/ghosh-2011-shallow-discourse-parsing-conditional-random/) 2. Alistair Knott. 1996. A data-driven methodology for motivating a set of coherence relations. [find](https://scholar.google.com/scholar?q=Alistair+Knott%2E+1996%2E+A+data%2Ddriven+methodology+for+motivating+a+set+of+coherence+relations%2E) 3. Fang Kong, Hwee Tou Ng, and Guodong Zhou. 2014. A constituent-based approach to argument labeling with joint inference in discourse parsing. 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