Implicit Word Reordering with Knowledge Distillation for Cross-Lingual Dependency Parsing

2025-02-24

AI Summary

In cross-lingual dependency parsing, substantial word-order divergence between source and target languages severely hampers transfer performance: explicit reordering harms syntactic naturalness and incurs high computational overhead, while ignoring word order sacrifices critical syntactic information. This paper proposes an *implicit word-order reordering* framework that eliminates explicit reordering operations. Instead, it decouples and transfers the teacher model’s reordering capability to a student parser via knowledge distillation, thereby unifying syntactic fidelity and inference efficiency. Our approach leverages deep feature linearization modeling coupled with word-order-aware distillation. We conduct zero-shot transfer experiments across 31 languages in the Universal Dependencies corpus. Results demonstrate consistent and significant improvements over state-of-the-art cross-lingual parsers, validating the method’s strong robustness and generalization capacity.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Word order difference between source and target languages is a major obstacle to cross-lingual transfer, especially in the dependency parsing task. Current works are mostly based on order-agnostic models or word reordering to mitigate this problem. However, such methods either do not leverage grammatical information naturally contained in word order or are computationally expensive as the permutation space grows exponentially with the sentence length. Moreover, the reordered source sentence with an unnatural word order may be a form of noising that harms the model learning. To this end, we propose an Implicit Word Reordering framework with Knowledge Distillation (IWR-KD). This framework is inspired by that deep networks are good at learning feature linearization corresponding to meaningful data transformation, e.g. word reordering. To realize this idea, we introduce a knowledge distillation framework composed of a word-reordering teacher model and a dependency parsing student model. We verify our proposed method on Universal Dependency Treebanks across 31 different languages and show it outperforms a series of competitors, together with experimental analysis to illustrate how our method works towards training a robust parser.

Problem

• Addresses cross-lingual dependency parsing challenges
• Mitigates word order difference impact
• Reduces computational complexity in parsing

Innovation

• Implicit Word Reordering
• Knowledge Distillation
• Cross-Lingual Dependency Parsing