Choosing Transfer Languages for Cross-Lingual Learning

Abstract: Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of NLP on low-resource languages. However, given a particular task language, it is not clear which language to transfer from, and the standard strategy is to select languages based on ad hoc criteria, usually the intuition of the experimenter. Since a large number of features contribute to the success of cross-lingual transfer (including phylogenetic similarity, typological properties, lexical overlap, or size of available data), even the most enlightened experimenter rarely considers all these factors for the particular task at hand. In this paper, we consider this task of automatically selecting optimal transfer languages as a ranking problem, and build models that consider the aforementioned features to perform this prediction. In experiments on representative NLP tasks, we demonstrate that our model predicts good transfer languages much better than ad hoc baselines considering single features in isolation, and glean insights on what features are most informative for each different NLP tasks, which may inform future ad hoc selection even without use of our method. Code, data, and pre-trained models are available at https://github.com/neulab/langrank

• The paper presents LANGRANK, a model that selects optimal transfer languages for low-resource NLP tasks by integrating both dataset-specific and linguistic features.
• Using gradient boosted decision trees and leave-one-out cross-validation, the study shows LANGRANK outperforms baseline methods across tasks like machine translation, entity linking, POS tagging, and dependency parsing.
• Feature importance analysis reveals that dataset statistics mainly drive performance in machine translation while linguistic metrics are critical for tasks such as entity linking and dependency parsing.

An Examination of Cross-Lingual Transfer Language Selection

The paper, "Choosing Transfer Languages for Cross-Lingual Learning" by Lin et al., provides a systematic approach to selecting optimal transfer languages for various NLP tasks involving low-resource languages. The overarching goal is to advance the efficacy of cross-lingual transfer by predicting which high-resource language can best serve as a transfer partner for a given low-resource language in specific tasks. This involves leveraging multiple features to evaluate potential transfer languages, rather than relying on ad hoc selection based on intuition or isolated criteria.

Theoretical Framework

This research positions the language selection task as a ranking problem, where languages are ranked based on their utility as transfer languages for an NLP task in a low-resource language. The authors introduce LANGRANK, a model that utilizes a set of features to predict the optimal transfer languages. These features include both dataset-dependent statistics (e.g., dataset size, word overlap, type-token ratio) and dataset-independent linguistic metrics derived from the URIEL Typological Database (e.g., geographical, genetic, syntactic, phonological distances).

Methodology and Experimental Setup

The evaluation consists of applying LANGRANK to four NLP tasks: machine translation (MT), entity linking (EL), part-of-speech tagging (POS), and dependency parsing (DEP). For each of these tasks, the authors employ gradient boosted decision trees (GBDT) trained with LambdaRank as their learning method, chosen for its ability to perform well with limited features and data.

Notably, the paper conducts its evaluation using a leave-one-out cross-validation approach, measuring performance with Normalized Discounted Cumulative Gain (NDCG) to assess the ranking of transfer languages. The results are compared with a range of baselines, where transfer languages are selected based on single features such as lexical similarities or typological distances.

Results and Analysis

LANGRANK outperforms all baseline methods across the four tasks, indicating its ability to predict more suitable transfer languages by integrating multiple attributes simultaneously. Among tasks, the feature importance analysis reveals that dataset statistics are especially critical for MT, whereas linguistic distance features are more decisive for EL and DEP. The findings suggest that different features carry varying weights depending on the task—information that is invaluable for guiding future cross-lingual transfers.

Implications and Future Developments

The implications of this research are both practical and theoretical. Practically, LANGRANK provides a framework that can significantly reduce the trial-and-error involved in selecting a transfer language, thus optimizing the computational resources expended in NLP experimentation. Theoretically, the insights gained from the feature importance analysis could lead to more informed heuristic approaches, even in the absence of comprehensive data required by LANGRANK itself.

Future research directions might explore extending this methodology to other NLP tasks or improving the interpretability and generalizability of the ranking model across a wider variety of languages. Moreover, integrating semi-supervised learning techniques could potentially enhance the model’s ability to handle scenarios with limited language corpus availability.

In summary, this paper makes a significant contribution to the field of cross-lingual transfer by providing an empirical method to systematically select the optimal transfer languages, encapsulating both linguistic typology and dataset properties, and setting a benchmark for further innovations in low-resource NLP.