bert对L2生产中的本地干扰敏感吗?

Proceedings of the Second Workshop on Insights from Negative Results in NLP Pub Date : 1900-01-01 DOI:10.18653/v1/2021.insights-1.6

Zixin Tang, P. Mitra, D. Reitter

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引用次数: 1

摘要

利用来自亚洲英语学习者国际语料库网络(ICNALE)和托福11语料库的论文部分，我们对基于BERT的神经语言模型进行了微调，以预测英语学习者的母语。结果表明，神经模型可以学习表征和检测这些母语影响，但多语言训练的模型在这方面没有优势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Are BERTs Sensitive to Native Interference in L2 Production?

With the essays part from The International Corpus Network of Asian Learners of English (ICNALE) and the TOEFL11 corpus, we fine-tuned neural language models based on BERT to predict English learners’ native languages. Results showed neural models can learn to represent and detect such native language impacts, but multilingually trained models have no advantage in doing so.

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Proceedings of the Second Workshop on Insights from Negative Results in NLP

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