Delexicalised Multilingual Discourse Segmentation for DISRPT 2021 and Tense, Mood, Voice and Modality Tagging for 11 Languages (2021)

Abstract: This paper describes our participating system for the Shared Task on Discourse Segmentation and Connective Identification across Formalisms and Languages. Key features of the presented approach are the formulation as a clause-level classification task, a language-independent feature inventory based on Universal Dependencies grammar, and composite-verb-form analysis. The achieved F1 is 92% for German and English and lower for other languages. The paper also presents a clause-level tagger for grammatical tense, aspect, mood, voice and modality in 11 languages.

## introduction despite the important role of discourse segmentation for natural language processing (nlp), there is no clear-cut definition of what a discourse segment is. degand and simon (2009) determine the boundaries of discourse segments as the intersection of clause boundaries and prosodic boundaries, which means specifically that a discourse segment spans one or several clauses (clauses as minimal discourse segments had been proposed in preceding works, e.g. mann and thompson (1988)). we follow this approach and view discourse segmentation as a binary classification problem that predicts for a clause whether it is the start of a new discourse segment. working with only text makes it impossible to fully implement degand and simon (2009)'s approach and include features that capture prosody and prosodic change. instead, we represent clauses as morphosyntactic feature structures that capture grammatical roles (subject, object etc.), verbal categories and clause connectives, believing that the use of pronouns, the change of tense, aspect and mood, the presence of conjunctions and other linguistic features also signal segment boundaries. the shared task provides discourse-segmented treebanks for 11 languages. all datasets exist in the universal dependencies (ud) format (nivre in 13 of the datasets, only discourse segments are annotated: a token which is the begin of a new discourse segment is labelled with @xmath0. in the remaining three datasets, the full discourse connective is annotated: a token which is the begin of a new discourse segment/connective is labelled with @xmath1. the task for both annotation schemes is to identify the starts of discourse segments/connectives. in addition, the full connective should be identified for the latter scheme. five datasets are only made available in a delexicalised format to participants without a linguistic data consortium membership. ## universal morphosyntactic features the following subsections briefly describe how distinct syntactic units are represented in udg and what features are extracted for the shared task. ## classification we expect a high interdependence between the extracted features, which is why we use decision trees for the classification of clauses. a decision tree is a statistical classification method that can both learn such complex dependencies and also visualise them in an understandable manner. experiments with other classifiers, including complement naive bayes, random forest and multilayer perceptron, could not improve the performance over that of a simple decision tree. this suggests that the decision tree makes the best out of the available features. ## results and discussion tables 2 and 3 show the results in the parsed and the plain condition. numbers are the f1 scores for discourse segmentation or connective identification, depending on the dataset. for the monolingual experiments, the highest value in each column is boldfaced. for the multilingual experiments, the higher value on each test set is underlined. in the monolingual experiments, the f1 scores for parsed data are on average 3.5% higher than those for plain data. the best result on a test set is usually achieved by the system trained on the corresponding training set. the systems presented in this paper do not perform better than the best systems from disrpt test set deu. eng. eng. eng. eng. eus. fas. fra. nld. por. rus. spa. spa. tur. zho. zho. rst. pdtb. rst. rst. sdrt. rst. rst. sdrt. rst. rst. rst. rst. rst. pdtb. pdtb. rst. pcc pdtb gum rstdt stac ert prstc annodis nldt cstn rrt rststb sctb tdb cdtb sctb ## conclusion in this paper, we approached discourse segmentation as a clause-level classification task and represented clauses as delexicalised ud-based feature structures. while the approach works sufficiently on some datasets (e.g. german), the performance is generally lower than that of other approaches (cf. zeldes et al., 2019). a major reason for this sense disambiguation and semantic feature assignment. in the context of the shared task, we extended dönicke (2020)'s algorithm for the grammatical analysis of composite verb forms and created the language-specific resources to run it for all 11 languages. thus, we also contribute to the task of compound-verb analysis, which is (in contrast to morphological analysis) underrepresented in nlp. @xcite however, annotating data with grammatical features and testing the algorithm goes beyond the scope of participating in the shared task and is left to future work. our system is available at https://gitlab.gwdg.de/ tillmann.doenicke/disrpt2021-tmvm .