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 secondlanguage 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.

heading: 1 Introduction
text: 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 (Zeldes, 2017), 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 (Da Cunha and Iruskieta, 2010; Iruskieta et al., 2015; Cao et al., 2018; Cao, 2020), 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 (Nguyen et al., 2021; Liu et al., 2021) 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:
1. A parallel Russian annotation of a large and diverse English GUM RST corpus dubbed RRG, enabling the development and evaluation of cross-lingual RST models. This resource enables the development and evaluation of crosslingual RST models following the same annotation framework, addressing a critical gap in the field.
2. A unified end-to-end RST parser achieving state-of-the-art performance on diverse benchmarks in both English and Russian:
• English: RST-DT (53.0% end-to-end Full F1), GUM9.1 (47.9% F1 – En, 47.6% F1 – bilingual),
• Russian: RRT (45.3% F1), new RRG (44.6% F1 – Ru, 45.4% F1 – bilingual).
Data, code, and models are publicly available at https://github.com/tchewik/BilingualRSP.
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heading: 2 Related Work
text: Our work intersects with two key areas of RST parsing: end-to-end and cross-lingual approaches. We review prior research in this section.
Top-down Document-level RST Parsing The paradigm of top-down rhetorical parsing has recently emerged and is receiving significant attention for its exceptional capabilities for efficient endto-end analysis through a unified model. Zhang et al. (2020) proposed a top-down strategy for parsing rhetorical structure from a sequence of elementary discourse units (EDUs). An encoder-decoder module with an internal stack iteratively ranks the split points, ultimately assigning each EDU to its corresponding rhetorical role. To account for the variation in document structure context at different levels of granularity, Kobayashi et al. (2020) presented a multi-level tree construction approach developing distinct paragraph- and sentence-level discourse unit representations. Multiple monolingual language models were tested in this framework by Kobayashi et al. (2022). Koto et al. (2021) simplified the parsing by reformulating it as a sequence labeling for sequences of EDUs. Zhang et al. (2021a) proposed computing an additional loss based on the dissimilarity between 3D representations of both gold and predicted trees, guiding the latter towards closer alignment with the original structures. Addressing the limitations of previous methods, Nguyen et al. (2021) devised an end-toend document-level parsing model. This architecture presents two key advantages: (1) it seamlessly integrates tree construction and EDU segmentation through token-level splitting decisions, and (2) it employs beam search for non-greedy RST parsing. Liu et al. (2021) introduced a joint model where a shared LM encoder is employed for both segmentation and tree construction. The tree is built via attention over the sequence of EDUs within the current unit. We adopt this approach, with further details provided in Section 4.
Cross-lingual Rhetorical Parsing The qualitative comparison conducted by Iruskieta et al. (2015) laid the foundation for multilingual rhetorical structure analysis. Applied to a small parallel corpus across English, Spanish, and Basque (318 EDUs per language), their method revealed significant similarities in rhetorical structures between languages. Differences primarily manifested in segmentation (sentence-level discourse structure).
This insight inspired subsequent efforts to bridge the gap between languages. Cao et al. (2018) developed a Spanish-Chinese bilingual RST Treebank consisting of 50 texts per language with varying lengths (111-1774 words). Cao (2020) conducted a comparative analysis of Spanish and Chinese, identifying discourse marker and punctuation changes, EDU order variations, and EDU insertions as key contributors to sentence-level differences. Braud et al. (2017) laid the groundwork for cross-lingual parsing experiments by harmonizing RST treebanks across languages and introducing 18 unified coarse-grained rhetorical labels. Subsequent work by Iruskieta and Braud (2019) leveraged multilingual word embeddings to adapt mono- and multilingual parsers to the Basque with limited RST annotations. Liu et al. (2020, 2021) then developed a novel neural parser utilizing EDU-level machine translation (MT). These advancements, while addressing data sparsity, also reveal challenges like ensuring the rhetorical naturalness of the texts translated segment-by-segment. The recent Georgetown Chinese Discourse Treebank (GCDT) (Peng et al., 2022) offers RST annotations for 50 Chinese texts (9710 EDUs) spanning 5 of 10 genres found in the GUM corpus following the same relation inventory. Notably, 19 documents drawn from multilingual sources like Wikipedia, Wikinews, and wikiHow have English counterparts in GUM, although content and presentation may diverge across languages.

heading: 3 RST Corpora
text: This work employs three previous RST datasets for two languages: English RST-DT1 (Carlson et al., 2001) and GUM9.12 (Zeldes, 2017), Russian RuRSTreebank2.1 (Pisarevskaya et al., 2017). Furthermore, we suggest an additional parallel annotation for the Georgetown RST annotations (GUM9.1) in Russian. This section discusses the datasets and preprocessing steps.
The general corpora analysis outlined in Table 1 reveals differences between the corpora extending beyond variation in genres, tree sizes, and relation labels inventory. For instance, in the RST-DT corpus, 79.4% of non-elementary sentences3 (those
1https://catalog.ldc.upenn.edu/LDC2002T07; under an LDC license.
2https://github.com/amir-zeldes/gum/releases/ tag/V9.1.0; CC BY 4.0.
3Sentence boundary prediction was performed using spaCy (English) and razdel (Russian) libraries for consistency. This approach minimizes the impact of potential errors
containing at least one relation) are spanned by well-formed rhetorical subtrees. This high prevalence, along with explicit sentence and paragraph boundary annotation, fostered research on sentencelevel RST analysis (Soricut and Marcu, 2003; Joty et al., 2012; Nejat et al., 2017; Lin et al., 2019; Zhang et al., 2021b). GUM exhibits less frequent alignment between formal sentence boundaries and rhetorical subtrees. Moreover, GUM’s RST annotations used for parser training and evaluation exclude paragraph markers altogether, contrasting with the explicit boundaries present in RST-DT. These differences underscore that variations in the rhetorical structure, even within the same genre,4 stem not only from diverse relation sets and text sources, as Liu and Zeldes (2023) suggest, but also from differences in annotation principles.

heading: 3.1 Annotations for English
text: RST-DT The RST-DT corpus remains the primary benchmark for RST parsing, offering finegrained annotations for WSJ news articles of various lengths.
GUM The Georgetown University Multilayer corpus is an expending multi-genre corpus containing multiple layers of linguistic annotation, including RST. Featuring both written and spoken language across 12 genres, it remains the largest monolingual RST annotation corpus to date.

heading: 3.2 RRT (RuRSTreebank)
text: We exclude the scientific portion of the RuRSTreebank corpus in our experiments, as these are reported to be the first attempts at RST annotation for Russian following the earliest incompatible guidelines (Chistova et al., 2021). The resulting dataset comprises news articles and blogs from diverse sources. It includes 5 news sources and 17 blogs covering topics such as travel, life stories, IT, cos-
from the sentence splitters on the comparison of the datasets. 4See Appendix A for genre-wise comparison.
metics, health, politics, environment, and psychology. Despite the diversity, most documents are only partially annotated. Among the 233 document annotations, only one text is fully covered by a single tree; the remaining documents have random underannotations. The maximum number of trees in a single *.rs3 document reaches 42, with an average of 11.7 trees per document. This has influenced previous attempts to build a Russian parser (Chistova et al., 2021; Chistova and Smirnov, 2022), in which many efforts are directed towards predicting a lookalike forest for each full document. However, we emphasize the clear randomness of tree boundaries within the text, treating each connected tree as a separate document in our study.5 Our approach’s validity is implicitly supported by the absence of rhetorical relations for higher-level textual organization (such as HEADING or TOPIC-CHANGE) in the RRT. Additionally, we’ve observed that in corpora for other languages, the fully annotated tree often represents only a portion of the original text. Following established practices in end-to-end discourse parsing for RRT, we address inconsistencies in the assignment of specific relations documented by Pisarevskaya et al. (2017). The dictionary in Appendix B assists in remapping these relations during corpus preprocessing.

heading: 3.3 RRG
text: The Russian RST dataset from Georgetown University Multilayer corpus (RRG) was constructed by manually translating the RST annotations in GUM9.1.6 A single document required an investment of up to 2.5 hours, with the overall process consisting of:
5The original train/dev/test corpus splitting is preserved. The documents are only split into docname_part_*.rs3 files processed independently. Documents containing only a single EDU are excluded. Within the refined corpus used for experiments, 12.8% of trees are constructed of 2 to 4 elementary discourse units.
6The train/dev/test splitting employed in the GUM corpus is preserved.
Translation We prioritized manual translation for 213 English texts, ensuring literary accuracy and genre-specific adaptation. This approach differs from the common practice in cross-lingual RST research, which often relies on EDU-level machine translation. Additionally, we ensured the precise translation of established terms and references through thorough research. Furthermore, speaker gender was identified by examining audio recordings for the vlog and conversation parts of the corpus.
Rhetorical Structure Alignment The translated texts were manually aligned to the original structures unit-by-unit, following the guidelines for EDU segmentation in Russian developed for RRT.7 To ensure consistency, an expert adjusted the annotations considering translation nuances. We added or removed elementary discourse units from the tree based on the discourse segmentation in the Russian sentences. Rhetorical relations and nuclearity were assigned following the GUM RST annotation guidelines.8 Since our approach involved refining sentence-level relations rather than constructing trees from scratch, we anticipated minimal deviation in the annotation of rhetorical structure. As shown in Table 1, RRG contains 95.9% of the EDUs found in GUM. Analysis revealed a predominant pattern of N-to-One mappings between unaligned EDUs, primarily due to language-specific differences:
• English verbs sometimes translate naturally to Russian nouns, e.g., adding becomes добавление, and preventing translates to профилактика (Figure 1).
• Russian often favors active voice, collapsing passive constructions (e.g., "[there’s sufficient iodine] [added into the food supply]" becomes "в пищевые продукты поступает достаточное количество йода").
• Some RRG EDUs correspond to the reduced SAME-UNIT relations in GUM (see Appendix C for an illustration).
One-to-N mappings occur when a single EDU in GUM is split into multiple units in RRG. This is primarily observed with prepositional phrases and instances where the Russian syntax allows for greater variation (Figure 2).
7https://rstreebank.ru/eng 8https://wiki.gucorpling.org/gum/guidelines
Annotation Polishing Our efforts to detect and correct misassigned labels and misaligned EDUs in the RRG draft began with an examination of the class distribution. It helped us identify obvious annotation errors, including some inherited from the original English corpus (such as rare and unlikely classes like RESTATEMENT_SN). To further refine the annotations, we trained the RST label classifier for Russian proposed by Chistova et al. (2021) on the draft dataset. This classifier served as an outlier detection tool, allowing us to detect potentially mislabeled examples. Specifically, we focused on cases where the classifier confidently predicted an incorrect class and excluded the true (annotated) class from its top 3 most probable predictions. Following the GUM relation annotation guidelines, we fixed any corrupted structures identified through this analysis. This process also revealed minor inherited annotation inconsistencies, which we standardized in the final RRG dataset (see Figure 8 Appendix D for details).

heading: 4 End-to-End RST Parser
text: The rhetorical structure parsers suggested in recent years (Zhang et al., 2020; Kobayashi et al., 2020; Zhang et al., 2021a; Nguyen et al., 2021) 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.

heading: 4.1 Base Model
text: As a base end-to-end deep model, we use the DMRST (Liu et al., 2021) architecture visualized in Figure 3.
The framework consists of four main modules: (1) EDU segmentation via document-level labeling, (2) hierarchical EDU encoding, (3) span-splitting decoding for tree construction, and (4) nuclearityrelation prediction using a bi-affine classifier. The encoded EDU sequence is iteratively parsed during decoding, and the classifier predicts the nuclearity and relations between adjacent units. Training minimizes the dynamic weighted average (DWA) (Liu et al., 2019) of losses for EDU segmentation, tree structure parsing, and nuclearity+relation labeling.

heading: 4.2 Modifications to the Base Model
text: To improve end-to-end parsing performance, we introduce modifications to the base model, focusing primarily on EDU segmentation and encoding.
Segmentation: ToNy The BiLSTM-CRF segmenter known by this name (Muller et al., 2019) is a simple yet robust neural token labeler that took first place in the DISRPT 2019 shared task (Zeldes et al., 2019). The original DMRST parser implements a feedforward token classifier (with an additional similar classifier for the right neighbor only
for loss penalization).9 We replace the original DMRST segmentation module with a BiLSTMCRF layer without additional losses.
Local EDU Encoding: E-BiLSTM Rather than averaging subword embeddings for local EDU encoding as in the original method, we utilize another BiLSTM layer, which enables us to achieve better sequence encodings. The concatenation of hidden states at the final time step of each pass captures the context of the phrase more precisely than an average of its subword embeddings.
No augmentations One of the distinctive features of the original DMRST is data augmentation using corpora unification and EDU-level machine translation. However, we emphasize that annotated corpora for different languages can present different interpretations of RST with nuances in the tree constraints and relation definitions. Furthermore, EDU-level MT can result in unnatural discourse structures in the target language and offer little linguistic knowledge (although it can augment examples of some relations in the training set). Therefore, we do not consider either corpora unification or machine translation. Instead, we build a full parallel RST corpus with consistent relation inventory.
DWA Window Size Dynamic weighting is crucial for ensuring that each component of the parser receives the necessary attention during training:
Ltotal = 3∑
k=1
λkLk, wk(i− 1) = Lk(i− 1) Lk(i− 2) (1)
λk(i) = softmax( wk(i− 1) Temp )× 3, (2)
where the loss Ltotal is the DWA of task-specific losses with weights λi; wk are the relative descending rates for tasks 1 (segmentation), 2 (tree construction), and 3 (relation labeling), i is an iteration index, and Temp controls the softness of the task weighting. However, relying solely on the last two batches (Equation 1) is susceptible to local trend
9Directly comparing segmentation scores from the report with ToNy’s paper raises concerns due to differing methodological choices. DMRST employs a different pretrained language model, potentially augmented data, and document-level segmentation, contrasting with ToNy’s reliance on the StanfordNLP sentence splitter. Furthermore, the original ToNy functions as a standalone segmenter, while DMRST incorporates segmentation into its unified encoder training for joint optimization with tree construction.
amplification, especially with smaller batches encompassing rhetorical trees of varying sizes and complexities. To address this issue, we introduce a DWA window size parameter b:
wk(i− 1) = ∑b
j=1 Lk(i− j)∑2b j=b+1 Lk(i− j)
(3)
By analyzing a broader range of loss values, the model can effectively identify long-term trends and adjust task weights accordingly. This modification improved training stability with smaller batches, particularly on the RRT dataset comprising a large number of single-relation discourse trees.

heading: 5 Experimental Setup
text: In this study, we adopt the multilingual xlm-roberta-large10 (Conneau et al., 2020) model known for its exceptional zero-shot performance across discourse relation classification tasks in multiple languages (Kurfalı and Östling, 2021). 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.

heading: 6 Monolingual Evaluation and Discussion
text: 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.

heading: 6.1 Segmentation
text: Segmentation performance is shown in Table 3 alongside other metrics for end-to-end parsing.
English The previous best segmentation performance belongs to the DisCut11 method (Metheniti et al., 2023), achieving 97.6% F1 on RSTDT12 and 95.5% F1 on GUM9.0. Our improved
10MIT License. 11A simple token classifier for sentences on top of the XLM-RoBERTa-large. 12Inter-annotator agreement for segmentation on a subset of 53 (Carlson et al., 2001) double-annotated texts within the RST-DT corpus yielded a score of 98.3% F1 (Soricut and Marcu, 2003). However, this evaluation remains limited to a small part of the corpus that does not align with its test section.
DMRST+ToNy surpasses this on RST-DT with an average of 97.9% F1. The final model also outperforms our baseline on GUM9.1 reaching an average F1 score of 95.5% compared to 94.7%.
Russian Building upon the ToNy (2019) method, Chistova and Smirnov (2022) achieve an F1 score of 89.1% on the RRT2.1 corpus). The DISRPT shared tasks (2019; 2021; 2023) featured an early and flawed version of RRT, which had nonhierarchical annotations of academic genres. Thus, the performance in segmentation and relation classification reported for their version of the dataset is not consistent with the version used in the current work on end-to-end discourse parsing for Russian. The details on the current version (RRT2.1) are outlined in Section 3.2. While the architecture modifications did not significantly impact segmentation performance on the RRT, they consistently improved it on the RRG corpus, with an average increase from 96.3% F1 to 96.9% F1.

heading: 6.2 Assessing the Joint Model
text: Our experiment on joint training of segmentation and parsing modules within a unified architecture produced intriguing results, revealing a fundamental tension between the two tasks. Models with higher F1 scores on gold-standard segmentation (Table 2) performed worse on both segmentation and end-to-end parsing metrics than models with lower gold-segmentation scores but better utilization of their predicted segments (Table 3). This pattern suggests that the encoder representations are being pulled in two opposing directions during finetuning. Sentence segmentation relies heavily on local cues within sentences, leading segmentationoptimized models to develop encodings for finegrained syntactic patterns. However, building a document-level parse tree requires capturing longrange context and global relationships, demanding encodings that recognize complex discourse units. Therefore, directly comparing jointly trained models on gold-EDU trees may not be reliable in this scenario. The following discussion delves into the end-to-end parsing evaluated in Table 3.
English The enhanced models achieve state-ofthe-art results for end-to-end English RST parsing. Leveraging ToNy segmentation for the RSTDT dataset and both ToNy and BiLSTM EDU en-
The human agreement scores reported in Table 2 are obtained on the same part of the corpus (Joty et al., 2015).
coding for the GUM dataset, we obtain a substantial improvement in unlabeled tree construction, measured by the Span metric (average increase of 0.8% for RST-DT and 1.9% for GUM). This gain is noteworthy considering the widespread use of unlabeled rhetorical trees in RST parsing applications (Guzmán et al., 2014; Khosla et al., 2021). Nuclearity assignment, crucial for tasks like summarization and sentiment analysis (Goyal and Eisenstein, 2016; Fu et al., 2016; Huber and Carenini, 2020), also benefits from our approach. The best models achieve an average F1-score of 64.8% (+0.7%) on RST-DT and 56.1% (+1.9%) on GUM for the Nuclearity metric. Finally, the full rhetorical structure construction for both datasets achieves 53.0% for RST-DT and 47.9% for GUM.
Russian While the enhanced model noticeably improved performance on other corpora, it surprisingly failed to do so on RRT. This disparity might be attributed to the overfitting of the ToNy segmenter, potentially caused by the larger batch size necessary for stable RRT training (Appendix E). Fewer EDUs per tree in RRT (Table 1) lead to shallower, less complex structures, maximizing the Span score for gold-standard segmentation (81.2% for the best model in Table 2). Building
trees from EDUs predicted with 92% F1 (Table 3) significantly drops the Span metric (15% F1 gap). Similar to the original GUM corpus, the model incorporating both modifications achieved the best results on RRG, exhibiting an average Full end-toend F1-score of 44.6%.

heading: 7 Cross-Dataset Compatibility in Russian RST Parsing
text: 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 Chistova et al. (2021). 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 (Cer et al., 2018). The neural classifier is based on the BiMPM architecture (Wang et al., 2017), and utilizes the ELMo model for Russian as well as pre-trained fastText embeddings (Bojanowski et al., 2017) and char-
acter n-gram embeddings to encode a discourse unit. The RRT dataset, which includes 24 classes, yielded a 48.9% macro F1 score, while the RRG dataset, which includes 27 classes, yielded a 46.3% macro F1 score (see Appendix F for detailed results). Cross-dataset classification results illustrated in Appendix F Figure 7 indicate a notable overlap among the majority of classes from the two datasets while also highlighting the challenge of RST treebanks unification across languages and frameworks.

heading: 8 Cross-Lingual Evaluation
text: 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 Table 4, the RST parser achieves remarkable results on Russian test documents in a zeroshot setting (0%), showcasing the strength of multilingual language models. It performs nearly on par with the monolingual parser specifically trained on Russian data (RRG, Table 3). Although the Russian parser exhibits improvements across all metrics (segmentation: +1.4%, Span splitting: +2.6%, Nuclearity assignment: +1.9%, Full: +2.4%), the gap remains relatively narrow, demonstrating the effectiveness of the original GUM-based parser across languages. Reversing the direction (Russian to English) revealed a substantial performance drop (Table 12, Appendix G). Its F1 score for English segmentation is only 86.9%. This disparity likely stems from heavy reliance on commas to separate elementary discourse units in Russian (examples in Figure 8, Appendix G). With only 18.5% of EDUs ending with commas in GUM compared to a staggering 37.5% in RRG, the segmenter became overly reliant on a feature less common in English.
Mixed Train Data The objective of this experiment is to estimate the data requirements for successful cross-lingual transfer in RST parsing, a task that relies on laborious expert annotation. We
evaluate cross-lingual transfer performance across different amounts of annotation, ranging from 25% to 100% of the target language corpus. Our evaluation considers an ideal scenario involving full parallel data. Table 4 presents the model’s performance as the number of labeled examples in the second language increases. We observe a gradual improvement in the model’s ability to construct rhetorical trees with attached nuclearities. However, the rhetorical labeling accuracy plateaus at approximately 50% of second language annotations. The genre-specific performance of the model is illustrated in Figure 4. A more detailed evaluation is provided in Appendix G. Genres such as wikihow, textbook, academic, voyage, bio (Wikipedia), speech, interview, and news exhibit the highest adaptation to the second language. Spoken discourse genres achieved the lowest parsing scores but showed notable adaptation (vlog: 33.3% to 36.6% F1; conversation: 22.1% to 27.4% F1).
The bilingual model outperforms the monolingual RRG model (44.6% F1), achieving a Full end-to-end score of 45.4% F1. This improvement might be attributed to the potential limitations of the pre-trained model, XLM-RoBERTa, in handling Russian due to the imbalanced nature of the CC-100 pre-training corpus (23.4B Russian tokens vs. 55.6B English tokens (Conneau et al., 2020)). Bilingual injection, where both languages are presented together during training, could help mitigate this imbalance by allowing the model to learn richer representations of Russian text. Despite a slight F1 decrease in English, the bilingual parser excelled in 9 out of 12 genres in Russian (as detailed in Table 5), highlighting the effectiveness of bilingual training for cross-lingual transfer.

heading: 9 Conclusion
text: 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.
Limitations
While the written sections of the corpus are welladapted to Russian, accurately capturing the nuances of Russian spontaneous speech in documents outlining English spoken discourse (vlog, conversation) through translation can be challenging. This presents an exciting opportunity for future research to explore the unique RST features of spoken discourse in Russian.

heading: Acknowledgements
text: The research was carried out using the infrastructure of the Shared Research Facilities “High Performance Computing and Big Data” (CKP “Informatics”) of FRC CSC RAS (Moscow). The research is supported by a grant for research centers in the field of artificial intelligence (agreement ID 000000D730321P5Q0002) with the Ivannikov Institute for System Programming of the Russian Academy of Sciences dated November 2, 2021 No. 70-2021-00142.

heading: A Sentence Subtrees Coverage
text: Examining tree-covered non-elementary sentences in the analyzed corpora (see Table 6) reveals evident disparities in formal structure between annotation schemas, even within the recurring news genre.
While (Soricut and Marcu, 2003) briefly mention a 95% coverage of sentences spanned by wellformed rhetorical subtrees in RST-DT, our analysis, based on automatic sentence segmentation and counting within binarized trees (the standard format for RST parsing), suggests a more conservative estimate of 86%. Notably, even among nonelementary sentences (those containing at least two elementary units) there remains a prevalence of 79.4% well-formed rhetorical trees in the corpus.

heading: B RRT Preprocessing Details
text: Table 7 provides information about the common renaming of mislabeled samples in RRT.
The mislabelings, which persist in version 2.1 and are consequently addressed during corpus preprocessing, can be attributed to the following factors:
• Relation selection errors. The Antithesis relation is intentionally excluded from the corpus during annotation. However, a few instances of this class within the corpus clearly imply the Attribution relation. Furthermore, Restatement_SN(NS), Preparation_NS, Elaboration_SN are considered impossible according to the annotation manual.
• Artifacts of shifting relation definitions. In pursuit of objectivity and annotation agreement, Pisarevskaya et al. (2017) combined or eliminated certain initial relations (cause, effect, motivation, evaluation, interpretation). Nevertheless, remnants of these fine-grained labels persist within the corpus.

heading: C RRG Construction Example
text: We use an additional example in Figure 5 to illustrate the details of the RRG creation process described in section 3.3.
Translation The first step involves translating the English sentence presented in Figure 5a into an academic Russian equivalent (Figure 5b). Machine EDU-level translation,13 as employed in related work, yields an incomprehensible sequence of unrelated phrases: [В этой статье мы сообщаем о новых открытиях]23 [методы слежения за глазами обеспечили]24 [в бессознательные процессы]25 [осмотр уникальной коллекции произведений искусства Зурбарана.]26. Manual translation, on the other hand, not only preserves coherence but also incorporates genrespecific adaptations to ensure alignment with established conventions of Russian academic writing. These adaptations include the use of academic
13DeepL is used for this example.
clichés and the passive voice. Additionally, factual adaptations ensure accurate translations of terms and names, such as eye-tracking to отслеживание движения глаз, and Zurbarán to Сурбаран.
Rhetorical Structure Alignment The order of EDUs differs between English and Russian. English EDUs 23, 25, and 26 combine into a single unit in Russian due to viewing translating to the noun просмотр. This collapses the SAME-UNIT relation, resulting in a direct alignment of the remaining ELABORATION_NS.

heading: D RRG Polishing Details
text: What How
(original name form; years of birth and death) joint-list
emojis separated from sentences evaluation-comment
"посвящённый ..." (devoted), "нацеленный ..." (targeting), and "направленный ..." (aimed)
purpose-attribute
"[также] известный как" ([also] known as) restatement-partial "Как я [уже] говорил(а), ..." (As I said,) organization-preparation
Table 8: Standardization of inconsistent annotations inherited from GUM9.1.
E Implementation Details
Table 9 shows the hyperparameters used in our experiments. The experiments are performed on an NVIDIA Tesla v100 GPU. A single run takes 4 to 8 GPU hours, depending on the dataset and batch size.
RST-DT GUM RRG RRT
batch size (# of trees) 2 1 1 6 bDWA (# of trees) 12 12 12 24
LM
hidden size 1024 sliding window length 400 learning rate 2e-05
Parser
hidden size 1024 1024 1024 768 dropout (segmenter input) 0.4 dropout (encoder input) 0.5 learning rate 1e-04
ToNy
hidden size 200
E-BiLSTM
hidden size 512
Table 9: Parameters used in the experiments.

heading: F Relation Classification Results
text: Table 10 presents a detailed rhetorical relation classification performance for each corpus employing a standalone classifier. The task is treated in the context of the end-to-end system, with merged relation and nuclearity. Figure 6 shows confusion matrices for the same classification models focusing only on the coarse-grained relation. Although the RRG-trained classifier achieved better performance for some mirroring relations (CONTINGENCY/CONDITION, PURPOSE, TOPIC/SOLUTIONHOOD), it struggled with causal relations (16.7% for RRG’s CAUSAL compared to 46.8% for RRT’s CAUSE-EFFECT). This can be attributed to the classifier’s reliance on discourse cues, as only 23.6% of DU pairs in RRG with a causal cue represent an actual causal relation, compared to 47.7% in RRT. Notably, EXPLANATION (13.9%), ELABORATION (11.6%), JOINT (10.0%), and CONTEXT (8.4%) are the most prevalent noncausal relations with causal markers in the RRG corpus.
Overlapping RST relation_nuclearity classes across two corpora are illustrated in Figure 7. Confidently predicted relations (entropy >75th percentile) are shown on the right, with the target corpus’s ground truth relations on the left. Only
frequent transitions (>2.5% of gold class) are included. These figures reveal recurring patterns of overlapping relations in the two annotation types. The classes ORGANIZATION_NS, MODE, CONTEXT_SN, and ORGANIZATION_NS in the RRG corpus do not correspond with certain classes in RRT when examining the mentioned discourse unit features. The RRT-trained classifier consistently assigns the CONDITION class to both RRG’s CONTINGENCY (contingency-condition) and CONTEXT (context-circumstance) classes. For parsing effi-
ciency, RRG merges its specific adversative classes (antithesis, concession, contrast) into a single ADVERSATIVE category. This unified category maps to two distinct relations in the RRT: CONTRAST and CONCESSION, leading to inconsistencies in nuclearity correspondence. The classifiers exhibit similar error patterns across both corpora. For instance, despite having its own dedicated Evidence relation within the broader EXPLANATION category, the RRG classifier consistently misidentifies the RRT’s EVIDENCE samples as ATTRIBUTION, mirroring 14% of the RRT classifier’s predictions. This suggests a bias in both models towards interpreting
references to information sources as attributions, regardless of the intended meaning. Meanwhile, RRT’s CAUSE-EFFECT class absorbs EXPLANATION’s Justify and Motivation, encompassing both event causality and justifications (except for EVIDENCE).

heading: G Genre-wise Evaluation
text: Tables 11, 12, and 13 provide detailed performance metrics for the end-to-end RST parsing in both languages. The monolingual Russian parser, when applied to English text in the zero-shot setting (Table 12), exhibits segmentation errors illustrated in Figure 8.

heading: Future Work
text: This presents an exciting opportunity for future research to explore the unique RST features of spoken discourse in Russian