D ia DP @ XLLM 25: Advancing C hinese Dialogue Parsing via Unified Pretrained Language Models and Biaffine Dependency Scoring (2025)

Abstract: Dialogue-level dependency parsing is crucial for understanding complex linguistic structures in conversational data, yet progress has been hindered by limited annotated resources and inadequate modeling of dialogue dynamics. Existing methods often fail to capture both intra- and inter-utterance dependencies effectively, particularly in languages like Chinese with rich contextual interactions. To address these challenges, we propose InterParser, a novel framework that integrates a pretrained language model (PLM), bidirectional GRU (BiGRU), and biaffine scoring for comprehensive dependency parsing. Our model encodes token sequences using a PLM, refines representations via deep BiGRU layers, and employs separate projections for “head” and “dependent” roles to optimize arc and relation prediction. For cross-utterance dependencies, speaker-specific feature projections are introduced to enhance dialogue-aware scoring. Joint training minimizes cross-entropy losses for both intra- and inter-utterance dependencies, ensuring unified optimization. Experiments on a standard Chinese benchmark demonstrate that InterParser significantly outperforms prior methods, achieving state-of-the-art labeled attachment scores (LAS) for both intra- and inter-utterance parsing.

## introduction dialogue-level dependency parsing is crucial for enhancing the capabilities of dialogue understanding systems. this approach seeks to create a unified tree structure that captures both intra-sentence syntactic dependencies and inter-utterance discourse relations. while sentence-level dependency parsing has been extensively researched in languages such as english and chinese @xcite , applying this approach to multi-turn * corresponding author dialogues presents unique challenges. dialogues inherently involve complex hierarchical interactions: within utterances, there are syntactic dependencies (e.g., subject-verb-object structures), and across utterances, there are discourse dependencies (e.g., question-answer pairs or causal reasoning). in chinese, a language characterized by flexible word order and context-dependent semantics, performing such hierarchical parsing is particularly challenging.the root nodes of each subtree are often predicates that reflect single semantic events, illustrated in figure 1 . recent advancements have started to bridge this gap. @xcite initiated the development of the chinese dialogue-level dependency treebank (cddt), which integrates syntactic dependencies from existing treebanks @xcite and discourse relations based on rhetorical structure theory (rst). they proposed rule-based signal detection and pseudo-labeling strategies to address data scarcity in resource-limited scenarios. however, this method's reliance on heuristic transformations, such as mapping syntactic 'root' nodes to discourse dependencies, and its multi-step pipeline process, can lead to error propagation and limited generalization. subsequently, @xcite introduced a large language model (llm)-assisted data augmentation technique, generating synthetic dialogues through various perturbations at the word, syntax, and discourse levels. although effective, this approach necessitates extensive prompt engineering and can struggle to maintain structural consistency between the generated text and the corresponding dependency labels. to address these limitations, we introduce an innovative end-to-end neural architecture tailored for chinese dialogue-level dependency parsing. this approach circumvents the need for intermediate rule-based procedures by integrating the modeling of both intra-and inter-utterance dependencies through a cohesive feature learning mechanism. our framework specifically targets three pivotal challenges: hierarchical integration of linguistic features: effective dialogue parsing necessitates the concurrent modeling of various linguistic dimensions, including character-level, word-level, and utterance-level representations. this is especially crucial in languages like chinese, characterized by intricate morphological structures and word agglutination. our model strategically fuses these multifaceted features to optimize the representation of both syntactic and discursive elements. speaker-aware interaction modeling: in multi-party dialogues, comprehending the roles and interactions between participants is essential. our method incorporates explicit modeling of speaker roles to capture dependencies that are unique to different interlocutors, such as those between a customer and a service agent. this aspect is often underrepresented in conventional dependency parsing methodologies. enhancing low-resource robustness: the scarcity of annotated dialogue data presents a significant challenge in training reliable models, particularly in low-resource settings. our model addresses this issue by leveraging syntactic priors from existing treebanks, while meticulously preventing overfitting to sparse discourse patterns. our contributions include the following key innovations: our model, evaluated on the cddt benchmark, achieves state-of-the-art performance in chinese dialogue-level dependency parsing. it effectively captures both syntactic and discourse dependencies, surpassing existing heuristic-based and multi-step pipeline methods. ## method to address the speed and performance inefficiencies of traditional sentence parsing models, especially when dealing with an increasing number of words and dependency parsing tags, we employ a hierarchical decoding strategy for inner-edu and inter-edu dependencies. additionally, we utilize the chinese-electra-180g-base-discriminator for pre-training our large model and incorporate a state-of-the-art biaffine parser @xcite to enhance parsing efficiency. our modelling framework is shown in figure 2 first, we are provided with an input dialogue text, represented as a sequence of n words @xmath0, w 2 , . . . , ## conclusion in this study, we introduce interparser, an innovative end-to-end framework designed for chinese dialogue-level dependency parsing. our model in-tegrates a pretrained language model, hierarchical bigru encoding, and speaker-aware biaffine scoring mechanisms, effectively merging intra-edu syntactic dependencies with inter-edu discourse relations. experimental outcomes showcase notable enhancements over existing techniques, reaching state-of-the-art performance with 92.56% uas for inner-edu parsing and 90.66% las for inner-edu parsing in few-shot scenarios. the ablation study further confirms the essential contribution of the bigru layer in capturing sequential linguistic structures.