Cross-Document Cross-Lingual NLI via RST -Enhanced Graph Fusion and Interpretability Prediction (2025)

Abstract: Natural Language Inference (NLI) is a fundamental task in natural language processing. While NLI has developed many sub-directions such as sentence-level NLI, document-level NLI and cross-lingual NLI, Cross-Document Cross-Lingual NLI (CDCL-NLI) remains largely unexplored. In this paper, we propose a novel paradigm: CDCL-NLI, which extends traditional NLI capabilities to multi-document, multilingual scenarios. To support this task, we construct a high-quality CDCL-NLI dataset including 25,410 instances and spanning 26 languages. To address the limitations of previous methods on CDCL-NLI task, we further propose an innovative method that integrates RST-enhanced graph fusion with interpretability-aware prediction. Our approach leverages RST (Rhetorical Structure Theory) within heterogeneous graph neural networks for cross-document context modeling, and employs a structure-aware semantic alignment based on lexical chains for cross-lingual understanding. For NLI interpretability, we develop an EDU (Elementary Discourse Unit)-level attribution framework that produces extractive explanations. Extensive experiments demonstrate our approach”s superior performance, achieving significant improvements over both conventional NLI models as well as large language models. Our work sheds light on the study of NLI and will bring research interest on cross-document cross-lingual context understanding, hallucination elimination and interpretability inference. Our dataset and code are available at https://anonymous.4open.science/r/CDCL-NLI-637E/ for peer review.

## introduction natural language inference (nli) is a fundamental task in natural language processing, aiming to determine the logical relationship between the given premise and hypothesis pair @xcite . while traditional nli tasks primarily deal with singlelanguage, short-text validations @xcite , document-level nli @xcite expands the scope of nli to longer contexts. however, the increasing globalization of information flow requires even more sophisticated inference capabilities across both language and document boundaries. in this paper, we introduce cross-document cross-lingual natural language inference (cdcl-nli), a novel paradigm extending traditional nli to multi-document and multilingual settings. figure 1 illustrates that cdcl-nli jointly reasons over premise documents in english and french to verify the hypothesis. the correct entailment prediction relies on integrating complementary information from both documents. while cdcl-nli addresses a real-world task with broad applications, it faces key challenges: 1) lack of existing datasets, which necessitates the construction of new resources to support research. 2) multilingual semantic alignment, requiring resolution of grammatical and conceptual differences across languages while preserving semantic consistency @xcite . 3) cross-document structure alignment, essential for capturing structural correspondences and implicit logical relations between documents of varying complexity @xcite ; and 4) interpretability, demanding transparent reasoning processes and verifiable confidence in inference outcomes @xcite . to address the first challenge, we curated a cdcl-nli dataset through collecting diverse premise documents from globesumm @xcite , generating hypotheses with gpt-4o (ope-nai, 2024) using customized prompts to ensure label diversity and balance and manually reviewing hypotheses and annotated explanations. the dataset contains 25,410 samples spanning 26 languages and 370 events. to address the rest challenges, we proposed a novel method that comprises three key components. 1) graph construction module: this component promotes semantic alignment by fusing graphs based on lexical chains, effectively linking semantically related concepts across documents. 2) graph representation module: utilizing an rst-enhanced relation-aware graph attention network (rgat) @xcite , this module supports structure alignment by capturing hierarchical discourse structures and cross-document dependencies through multi-head attention mechanisms. extensive experiments on the cdcl-nli and docnli datasets demonstrate that our method out-performs conventional nli approaches and three state-of-the-art large language models, surpassing the strongest baseline by 3.5% on our dataset. in the end, we highlight our main contributions as follows: ## cdcl-nli task formulation and dataset construction as shown in figure 2 , our cdcl-nli dataset is constructed through a systematic pipeline involving stratified random sampling of premise documents across all topics, llm-generated hypotheses, and human verification to ensure data quality. in the dashed box, the figure shows a cdcl-nli instance with a premise of two documents in different languages, an english hypothesis, a label, and edubased explanations for interpretability. ## our method: rst-enhanced graph fusion with edu level interpretability our approach offers a robust solution for crossdocument and cross-lingual nli by leveraging rst-enhanced graph fusion and explanation prediction. as illustrated in figure 4 , the framework comprises three main components: rst graph construction and fusion module, graph representation generation module and interpretability and classification module. we employ dm-rst @xcite parser for discourse modeling as it offers an optimal balance between structural richness and computational feasibility. compared to the locally-focused pdtb @xcite , rst's hierarchical structure effectively captures document-level organization essential for cross-document reasoning. while sdrt @xcite is semantically richer, its o(n 3 ) complexity is prohibitive for large-scale tasks. our ablation study ( ## conclusion this work systematically investigates cdcl-nli, addressing key challenges in cross-document reasoning and multilingual understanding. we introduce a novel cdcl-nli dataset spanning 26 languages and comprising 25,410 meticulously annotated instances. and we propose an rstenhanced graph fusion mechanism with explanation prediction. through extensive experiments and analyses, we demonstrate that our method effectively captures both structural and semantic information across documents and languages. specifically, the rst-enhanced graph fusion mechanism and explanation prediction component not only improve model interpretability but also enhance performance, as validated by our ablation studies.