Awesome

Chinese NLI Probing

Introduction

This folder contains data and code for the paper Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference, where we examine the cross-lingual transfer learning in XLM-R via 4 categories of newly constructed NLI datasets:

• Chinese HANS
• Chinese stress tests
• Expanded CLUE diagnostics
• Chinese semantic fragments

All of them are made in parallel to existing adversarial/probing datasets in English (but from scratch, i.e., no translations), so as to allow cross-lingual transfer studies in both directions: English-to-Chinese and Chinese-to-English.

Data

The generated data can be found in the data folder, with detailed explanations.

Code

The code used for data generation can be found in the code folder (will be uploaded shortly).

Main results

We find thatcross-lingual models trained on English NLI do transfer well across our Chinese tasks (e.g., in 3/4 of our challenge categories, they per-form as well/better than the best monolingual models, even on 3/5 uniquely Chinese lin-guistic phenomena such as idioms, pro drop). These results, however, come with importantcaveats: cross-lingual models often performbest when trained on a mixture of English and high-quality monolingual NLI data (OCNLI), and are often hindered by automatically trans-lated resources (XNLI-zh). For many phenomena, all models continue to struggle, highlighting the need for our new diagnostics to help benchmark Chinese and cross-lingual models.

(will be expanded shortly)

References

If you use our resources or find our results useful, please cite:

@inproceedings{hu-et-al-2021-investigating,
	title={Investigating Transfer Learning in Multilingual Pre-trained Language Models through {Chinese} Natural Language Inference},
	author={Hai Hu and He Zhou and Zuoyu Tian and Yiwen Zhang and Yina Ma and Yanting Li and Yixin Nie and Kyle Richardson},
	booktitle={Findings of ACL},
	year={2021}
}