Paper Info
Title
Does Meta-learning Help mBERT for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages?
Abstract
Few-shot Question Generation (QG) is an important and challenging problem in the Natural Language Generation (NLG) domain. Multilingual BERT (mBERT) has been successfully used in various Natural Language Understanding (NLU) applications. However, the question of how to utilize mBERT for few-shot QG, possibly with cross-lingual transfer, remains. In this paper, we try to explore how mBERT performs in few-shot QG (cross-lingual transfer) and also whether applying meta-learning on mBERT further improves the results. In our setting, we consider mBERT as the base model and fine-tune it using a seq-to-seq language modeling framework in a cross-lingual setting. Further, we apply the model agnostic meta-learning approach to our base model. We evaluate our model for two low-resource Indian languages, Bengali and Telugu, using the TyDi QA dataset. The proposed approach consistently improves the performance of the base model in few-shot settings and even works better than some heavily parameterized models. Human evaluation also confirms the effectiveness of our approach.
Year
Venue
DocType
2022
International Conference on Computational Linguistics
Conference
Volume
Citations 
PageRank 
Proceedings of the 29th International Conference on Computational Linguistics
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Aniruddha Roy100.34
Rupak Kumar Thakur200.34
Isha Sharma300.34
Ashim Gupta400.68
Amrith Krishna565.91
Sudeshna Sarkar6423210.58
Pawan Goyal71670210.04