AlignXIE: Improving Multilingual Information Extraction by Cross-Lingual Alignment (2411.04794v1)

Abstract: Empirical evidence suggests that LLMs exhibit spontaneous cross-lingual alignment. Our findings suggest that although LLMs also demonstrate promising cross-lingual alignment in Information Extraction, there remains significant imbalance across languages, revealing an underlying deficiency in the IE alignment. To address this issue, we propose AlignXIE, a powerful code-based LLM that significantly enhances cross-lingual IE alignment through two strategies. Firstly, AlignXIE formulates IE across different languages, especially non-English ones, as code generation tasks, standardizing the representation of various schemas using Python classes to ensure consistency of the same ontology in different languages and align the schema. Secondly, it incorporates an IE cross-lingual alignment phase through a translated instance prediction task proposed in this paper to align the extraction process, utilizing ParallelNER, an IE bilingual parallel dataset with 257,190 samples, generated by our proposed LLM-based automatic pipeline for IE parallel data construction, with manual annotation to ensure quality. Ultimately, we obtain AlignXIE through multilingual IE instruction tuning. Although without training in 9 unseen languages, AlignXIE surpasses ChatGPT by $30.17\%$ and SoTA by $20.03\%$, thereby demonstrating superior cross-lingual IE capabilities. Comprehensive evaluations on 63 IE benchmarks in Chinese and English under various settings, demonstrate that AlignXIE significantly enhances cross-lingual and multilingual IE through boosting the IE alignment.

AlignXIE: Enhancing Multilingual Information Extraction through Cross-Lingual Alignment

The paper "AlignXIE: Improving Multilingual Information Extraction by Cross-Lingual Alignment" introduces a novel framework designed to enhance cross-lingual capabilities in multilingual information extraction (IE). Recognizing the inherent imbalances across languages in traditional LLMs, the authors propose AlignXIE, a code-based LLM framework that utilizes cross-lingual alignment to improve performance in information extraction tasks.

Core Contributions

AlignXIE introduces two principal strategies aimed at improving cross-lingual alignment in information extraction:

1. Unified Code Generation Framework:
   • AlignXIE treats information extraction as a code generation task by using Python classes to standardize schema representations across different languages. This approach emphasizes leveraging the language-agnostic properties of code to ensure consistent semantic representation across language boundaries. Python classes provide a uniform template, which simplifies the integration and alignment of schema representations across typologically diverse languages.
2. Cross-Lingual Alignment Phase:
   • The alignment phase enhances the extraction process by employing a translated instance prediction task. It leverages ParallelNER, a bilingual parallel dataset, to guide the learning process. Importantly, this dataset is constructed using an LLM-based pipeline to ensure high quality through contextual translation and rephrasing techniques. This phase is crucial in allowing the model to generalize and align information extraction capabilities across unseen languages, as evidenced by experiments showing substantial improvements over existing models such as ChatGPT and state-of-the-art (SoTA) systems.

Experimental Insights

The experimental validation of AlignXIE demonstrates its capability to handle 63 IE benchmarks spanning multiple languages and settings effectively. The model achieved remarkable gains, outperforming ChatGPT by 30.17% and SoTA by 20.03% in cross-lingual settings. Additionally, in supervised evaluations, AlignXIE consistently ranked within the top-2 results across 40 out of 42 benchmarks, highlighting its robust performance across both Named Entity Recognition (NER) and Relation Extraction (RE) tasks in English and Chinese.

Theoretical and Practical Implications

Theoretically, AlignXIE underscores the potential of using code-based representations to unify and improve multilingual tasks. By ensuring schema and extraction consistency, the approach reduces semantic drift across languages. Practically, AlignXIE provides a framework that can be reused and adapted for new languages with minimal resource requirements, offering a scalable solution applicable to a variety of multilingual environments.

Future Directions

Future work might involve expanding the AlignXIE model to include a broader range of languages beyond the current focus on English and Chinese. Additionally, integrating other information extraction tasks such as Event Detection (ED) and Event Argument Extraction (EAE) during the cross-lingual alignment phase could further enhance the model's capabilities.

In conclusion, AlignXIE represents a significant step forward in refining multilingual IE systems, effectively bridging the gap between schema alignment and cross-lingual transfer, thereby optimizing performance in complex multilingual settings.