GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.Comment: Proceedings of Discourse Relation Parsing and Treebanking (DISRPT2019

Elaboration of a RST Chinese Treebank

[EN] As a subfield of Artificial Intelligence (AI), Natural Language Processing (NLP) aims to automatically process human languages. Fruitful achievements of variant studies from different research fields for NLP exist. Among these research fields, discourse analysis is becoming more and more popular. Discourse information is crucial for NLP studies. As the most spoken language in the world, Chinese occupy a very important position in NLP analysis. Therefore, this work aims to present a discourse treebank for Chinese, whose theoretical framework is Rhetorical Structure Theory (RST) (Mann and Thompson, 1988). In this work, 50 Chinese texts form the research corpus and the corpus can be consulted from the following aspects: segmentation, central unit (CU) and discourse structure. Finally, we create an open online interface for the Chinese treebank.[EU] Adimen Artifizialaren (AA) barneko arlo bat izanez, Hizkuntzaren Prozesamenduak (HP) giza-hizkuntzak automatikoko prozesatzea du helburu. Arlo horretako ikasketa anitzetan lorpen emankor asko eman dira. Ikasketa-arlo ezberdin horien artean, diskurtso-analisia gero eta ezagunagoa da. Diskurtsoko inforamzioa interes handikoa da HPko ikasketetan. Munduko hiztun gehien duen hizkuntza izanda, txinera aztertzea oso garrantzitsua da HPan egiten ari diren ikasketetarako. Hori dela eta, lan honek txinerako diskurtso-egituraz etiketaturiko zuhaitz-banku bat aurkeztea du helburu, Egitura Erretorikoaren Teoria (EET) (Mann eta Thompson, 1988) oinarrituta. Lan honetan, ikerketa-corpusa 50 testu txinatarrez osatu da, ea zuhaitz-bankua hiru etiketatze-mailatan aurkeztuko da: segmentazioa, unitate zentrala (UZ) eta diskurtso-egitura. Azkenik, corpusa webgune batean argitaratu da zuhaitz-bankua kontsultatzeko

Cross-lingual and cross-domain discourse segmentation of entire documents

Discourse segmentation is a crucial step in building end-to-end discourse parsers. However, discourse segmenters only exist for a few languages and domains. Typically they only detect intra-sentential segment boundaries, assuming gold standard sentence and token segmentation, and relying on high-quality syntactic parses and rich heuristics that are not generally available across languages and domains. In this paper, we propose statistical discourse segmenters for five languages and three domains that do not rely on gold pre-annotations. We also consider the problem of learning discourse segmenters when no labeled data is available for a language. Our fully supervised system obtains 89.5% F1 for English newswire, with slight drops in performance on other domains, and we report supervised and unsupervised (cross-lingual) results for five languages in total.Comment: To appear in Proceedings of ACL 201

Cross-lingual RST Discourse Parsing

Discourse parsing is an integral part of understanding information flow and argumentative structure in documents. Most previous research has focused on inducing and evaluating models from the English RST Discourse Treebank. However, discourse treebanks for other languages exist, including Spanish, German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same underlying linguistic theory, but differ slightly in the way documents are annotated. In this paper, we present (a) a new discourse parser which is simpler, yet competitive (significantly better on 2/3 metrics) to state of the art for English, (b) a harmonization of discourse treebanks across languages, enabling us to present (c) what to the best of our knowledge are the first experiments on cross-lingual discourse parsing.Comment: To be published in EACL 2017, 13 page

A Pilot Study on Dialogue-Level Dependency Parsing for Chinese

Dialogue-level dependency parsing has received insufficient attention, especially for Chinese. To this end, we draw on ideas from syntactic dependency and rhetorical structure theory (RST), developing a high-quality human-annotated corpus, which contains 850 dialogues and 199,803 dependencies. Considering that such tasks suffer from high annotation costs, we investigate zero-shot and few-shot scenarios. Based on an existing syntactic treebank, we adopt a signal-based method to transform seen syntactic dependencies into unseen ones between elementary discourse units (EDUs), where the signals are detected by masked language modeling. Besides, we apply single-view and multi-view data selection to access reliable pseudo-labeled instances. Experimental results show the effectiveness of these baselines. Moreover, we discuss several crucial points about our dataset and approach.Comment: Accepted by Findings of ACL 2023 (Camera-ready version

Elaboration of a RST Chinese Treebank

[EN] As a subfield of Artificial Intelligence (AI), Natural Language Processing (NLP) aims to automatically process human languages. Fruitful achievements of variant studies from different research fields for NLP exist. Among these research fields, discourse analysis is becoming more and more popular. Discourse information is crucial for NLP studies. As the most spoken language in the world, Chinese occupy a very important position in NLP analysis. Therefore, this work aims to present a discourse treebank for Chinese, whose theoretical framework is Rhetorical Structure Theory (RST) (Mann and Thompson, 1988). In this work, 50 Chinese texts form the research corpus and the corpus can be consulted from the following aspects: segmentation, central unit (CU) and discourse structure. Finally, we create an open online interface for the Chinese treebank.[EU] Adimen Artifizialaren (AA) barneko arlo bat izanez, Hizkuntzaren Prozesamenduak (HP) giza-hizkuntzak automatikoko prozesatzea du helburu. Arlo horretako ikasketa anitzetan lorpen emankor asko eman dira. Ikasketa-arlo ezberdin horien artean, diskurtso-analisia gero eta ezagunagoa da. Diskurtsoko inforamzioa interes handikoa da HPko ikasketetan. Munduko hiztun gehien duen hizkuntza izanda, txinera aztertzea oso garrantzitsua da HPan egiten ari diren ikasketetarako. Hori dela eta, lan honek txinerako diskurtso-egituraz etiketaturiko zuhaitz-banku bat aurkeztea du helburu, Egitura Erretorikoaren Teoria (EET) (Mann eta Thompson, 1988) oinarrituta. Lan honetan, ikerketa-corpusa 50 testu txinatarrez osatu da, ea zuhaitz-bankua hiru etiketatze-mailatan aurkeztuko da: segmentazioa, unitate zentrala (UZ) eta diskurtso-egitura. Azkenik, corpusa webgune batean argitaratu da zuhaitz-bankua kontsultatzeko

Towards interoperable discourse annotation: discourse features in the Ontologies of Linguistic Annotation

This paper describes the extension of the Ontologies of Linguistic Annotation (OLiA) with respect to discourse features. The OLiA ontologies provide a a terminology repository that can be employed to facilitate the conceptual (semantic) interoperability of annotations of discourse phenomena as found in the most important corpora available to the community, including OntoNotes, the RST Discourse Treebank and the Penn Discourse Treebank. Along with selected schemes for information structure and coreference, discourse relations are discussed with special emphasis on the Penn Discourse Treebank and the RST Discourse Treebank. For an example contained in the intersection of both corpora, I show how ontologies can be employed to generalize over divergent annotation schemes