Applying Multilingual Models to Question Answering (QA)
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Abstract (arXiv)
We study the performance of monolingual and multilingual language models on the task of question-answering (QA) on three diverse languages: English, Finnish and Japanese. We develop models for the tasks of (1) determining if a question is answerable given the context and (2) identifying the answer texts within the context using IOB tagging. Furthermore, we attempt to evaluate the effectiveness of a pre-trained multilingual encoder (Multilingual BERT) on cross-language zero-shot learning for both the answerability and IOB sequence classifiers.
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References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2017Axiomatic Attribution for Deep Networkscited by 3,384
- 2020TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languagescited by 488
- 2019Multilingual is not enough: BERT for Finnishcited by 119
- 2017BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languagescited by 52
- 2021A Comparative Study of Transformer-Based Language Models on Extractive Question Answeringcited by 13
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