Bilingual Rhetorical Structure Parsing with Large Parallel Annotations (2024)

Abstract: Discourse parsing is a crucial task in natural language processing that aims to reveal the higher-level relations in a text. Despite growing interest in cross-lingual discourse parsing, challenges persist due to limited parallel data and inconsistencies in the Rhetorical Structure Theory (RST) application across languages and corpora. To address this, we introduce a parallel Russian annotation for the large and diverse English GUM RST corpus. Leveraging recent advances, our end-to-end RST parser achieves state-of-the-art results on both English and Russian corpora. It demonstrates effectiveness in both monolingual and bilingual settings, successfully transferring even with limited second-language annotation. To the best of our knowledge, this work is the first to evaluate the potential of cross-lingual end-to-end RST parsing on a manually annotated parallel corpus.

## introduction discourse parsing aims to reveal the higher-level organization of text. while the task has gained significant traction in recent years, cross-lingual rhetorical structure parsing remains a complex challenge. this stems from the inherent diversity of annotation schemes across languages within the rhetorical structure theory (rst) framework and the scarcity of parallel corpora. existing large rst corpora are inconsistent in annotation guidelines, genre representation, source selection, and relation definitions. therefore, current studies might underestimate the true potential of rst parsers for language transfer. this study addresses these challenges by introducing a russian version of the rst part of the georgetown university multilayer (gum) corpus @xcite , encompassing all 213 original documents. this large parallel corpus provides a valuable resource for bilingual discourse analysis, enabling the development of robust rst models that can effectively capture the rhetorical structure of text in both languages. as previous research suggests @xcite @xcite , differences in rhetorical structures across languages primarily arise at the lower structural levels, while the global document organization exhibits some universality. currently, topdown, unified-model frameworks @xcite have proven highly effective for end-to-end rst parsing. hypothetically, these parsers should begin by constructing a languageindependent high-level structure, with languagespecific nuances incorporated primarily at lower levels. this study investigates the effectiveness of an end-to-end top-down rst parser adaptation across genres in a second language, utilizing both monolingual and bilingual training data. recognizing the substantial cost of rst annotation, we further investigate the efficient amount of secondlanguage annotation for parser transfer. the main contributions of this work are: data, code, and models are publicly available at https://github.com/tchewik/bilingualrsp . ## related work our work intersects with two key areas of rst parsing: end-to-end and cross-lingual approaches. we review prior research in this section. ## rst corpora this work employs three previous rst datasets for two languages: english rst-dt foot_0 @xcite and gum 9.1 foot_1 @xcite , russian rurstreebank 2.1 @xcite . furthermore, we suggest an additional parallel annotation for the georgetown rst annotations (gum 9.1 ) in russian. this section discusses the datasets and preprocessing steps. ## end-to-end rst parser the rhetorical structure parsers suggested in recent years @xcite @xcite often focused on developing innovative features to address either specific aspects of the structure construction or its global optimization. however, these approaches often overlook the integration of previously established effective features. they also frequently neglect the end-to-end performance, a fundamental aspect of any practical framework. we are building a hybrid deep model solving both segmentation and tree construction that benefits from the techniques suggested by recent work. ## experimental setup in this study, we adopt the multilingual xlm-roberta-large 10 (conneau et al., 2020) model known for its exceptional zero-shot performance across discourse relation classification tasks in multiple languages @xcite . hyperparameters are fixed as specified in appendix e. we average results across five runs with varying model seeds (fixed-split corpora: gum and rrg, rrt) or different train/dev splits (rst-dt). bilingual experiments (section 8) additionally involve randomly selecting 25%, 50%, and 75% of the second-language data for each of the five runs. ## monolingual evaluation and discussion this section evaluates the monolingual parsing performance for two languages. our baseline dmrst (this work) differs from the dmrst (2021) by employing the xlm-roberta-large language model and dwa window size parameter. ## cross-dataset compatibility in russian rst parsing this section explores the cross-dataset compatibility of russian rst parsing by comparing two relation inventories derived from rrt and rrg parsers using a data-driven approach. relation labeling to categorize the discourse unit pairs connected in the annotated corpora, we trained the relation classifier for russian developed by @xcite . it is an ensemble of a feature-rich classifier and an elmo-driven classifier. the feature-rich classifier includes a comprehensive dictionary of discourse cues in russian, various morpho-syntactic features, a sentiment classifier, and use vectors @xcite . the neural classifier is based on the bimpm architecture @xcite , and utilizes the elmo model for russian as well as pre-trained fasttext embeddings @xcite and char- ## cross-lingual evaluation in this section, we explore the capabilities of our best +tony+e-bilstm model in two scenarios: (1) its performance on an unseen or under-annotated language, and (2) its bilingual adaptation when trained on a fully-annotated parallel corpus. we assess the performance of a model on a new language, analyzing how expanding the parallel training data influences its ability to parse diverse writing and speech styles. with the english training data held constant, we investigate its ability to adapt to different genres in russian. direct transfer by employing documents that differ only in language, we isolate the impact of language on rst parsing within zero-shot generaliza-tion, offering a more nuanced evaluation compared to typical mixed-source approaches. as demonstrated in ## conclusion this study addresses the challenges of cross-lingual discourse parsing. we introduce a large parallel russian annotation of the multigenre gum rst corpus and assess the performance of an end-toend top-down model in bilingual rhetorical structure parsing. the top-down unified parser employing a multilingual language model established a strong baseline on end-to-end parsing in both languages. further analysis explored direct parser transfer without second-language data. surprisingly, transferring the english parser to russian achieved comparable quality to the monolingual parser. however, the reverse transfer suffered due to nuances in russian discourse segmentation, underlining the critical role of language-specific features in language transfer. we investigated the effectiveness of porting the analyzer with limited second-language data. our findings demonstrate that even with minimal data, such transfer remains effective. finally, training the bilingual parser on the entire parallel dataset yielded the best discourse parsing performance in russian, and strong performance in english.