Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language (2004.12440v2)

Abstract: To better tackle the named entity recognition (NER) problem on languages with little/no labeled data, cross-lingual NER must effectively leverage knowledge learned from source languages with rich labeled data. Previous works on cross-lingual NER are mostly based on label projection with pairwise texts or direct model transfer. However, such methods either are not applicable if the labeled data in the source languages is unavailable, or do not leverage information contained in unlabeled data in the target language. In this paper, we propose a teacher-student learning method to address such limitations, where NER models in the source languages are used as teachers to train a student model on unlabeled data in the target language. The proposed method works for both single-source and multi-source cross-lingual NER. For the latter, we further propose a similarity measuring method to better weight the supervision from different teacher models. Extensive experiments for 3 target languages on benchmark datasets well demonstrate that our method outperforms existing state-of-the-art methods for both single-source and multi-source cross-lingual NER.

Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language

The paper "Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language" introduces an innovative approach to tackle named entity recognition (NER) in scenarios characterized by a lack of labeled data in the target language. This situation is common in low-resource languages, where the high cost of annotation imposes significant challenges. The proposed method diverges from traditional label-projection and direct model transfer techniques, which are limited by the availability of labeled data in source languages and the underutilization of unlabeled data in target languages.

Methodology Overview

The authors propose a teacher-student learning paradigm that effectively capitalizes on unlabeled target-language data for training NER models. The method leverages NER models trained in well-resourced source languages (teachers) to predict pseudo-labels for unlabeled data in the target language. Subsequently, a student model is trained on these pseudo-labeled target language datasets. This approach supports both single-source and multi-source cross-lingual NER.

For multi-source scenarios, the authors extend the methodology by incorporating a similarity-measuring component. This component weights the influence of each teacher model according to its linguistic similarity to the target language, ensuring that more relevant knowledge from similar languages is prioritized. The similarity computation is novel, leveraging an auxiliary task of language identification to derive cross-linguistic similarities using a bilinear model on language embeddings.

Experimental Verification

The effectiveness of this approach is corroborated by extensive experiments on standard benchmark datasets, namely CoNLL-2002 (for Spanish and Dutch) and CoNLL-2003 (for English and German). The results reveal that the proposed method outshines existing state-of-the-art techniques in both single-source and multi-source cross-lingual NER, delivering considerable performance gains.

• For single-source scenarios, empirically, the teacher-student learning approach yielded F1-score improvements over direct model transfer methods. These results underscore the method's capacity to extract and leverage useful knowledge from the target language's unlabeled corpora.
• In multi-source settings, incorporating language similarity measurements to weigh the contributions of disparate teacher models further refines the student model's accuracy. The results demonstrate this weighting strategy's advantage over a simple averaging of teacher model outputs.

Theoretical and Practical Implications

From a theoretical perspective, the paper contributes a valuable strategy to the cross-lingual knowledge transfer literature by integrating the teacher-student learning model, which respects both the inductive biases of source LLMs and the specificity of the target language derived from unlabeled data. This presents a paradigm shift in how unannotated data can be used to enhance NER tasks across languages.

In practical terms, the approach paves the way for deploying NER systems in languages with limited resources, broadening the accessible data domains for natural language processing applications. This is particularly crucial in advancing the reach and equity of NLP technologies globally by reducing dependency on high-resource languages.

Future Outlook

The promising outcomes suggest several directions for future research:

1. Extension to Other NLP Tasks: Teacher-student learning on unlabeled data could be beneficially extended to other NLP tasks, such as sentiment analysis or machine translation, where large unlabeled datasets are available.
2. Incorporating Additional Features: Exploring ways to integrate more diverse linguistic features or domain-specific knowledge into teacher-student frameworks could offer further performance enhancement.
3. Adaptive Similarity Measures: Developing more sophisticated adaptive mechanisms for calculating linguistic similarity, potentially involving deep learning models trained on large-scale multilingual datasets, could refine the multi-source knowledge transfer even further.

In conclusion, this research exemplifies a significant advancement in cross-lingual NER. It both broadens the methodological toolkit available for low-resource languages and demonstrates how innovative uses of task-agnostic models like BERT can transform language-specific challenges into adaptable, scalable solutions.