常见问题的跨语言转换器改编

Luca Di Liello, Daniele Bonadiman, Alessandro Moschitti, Cristina Giannone, A. Favalli, Raniero Romagnoli
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引用次数: 0

摘要

迁移学习已被证明是有效的,特别是当目标领域/任务的数据稀缺时。有时,类似任务的数据只能以另一种语言提供,因为它可能非常具体。在本文中,我们探索了使用机器翻译数据来转移相关领域的模型。具体来说,我们将模型从问题复制任务(QDT)转移到类似的FAQ选择任务。源域是众所周知的英语Quora数据集,而目标域是一个小型意大利语数据集的集合,这些数据集是由常见答案组成的常见问题解答组组成的真实案例场景。我们的研究结果表明,在零射击学习设置中有很大的改进,而在直接域内自适应的标准迁移方法中有适度的改进。
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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Cross-Language Transformer Adaptation for Frequently Asked Questions
Transfer learning has been proven to be effective, especially when data for the target domain/task is scarce. Sometimes data for a similar task is only available in another language because it may be very specific. In this paper, we explore the use of machine-translated data to transfer models on a related domain. Specifically, we transfer models from the question duplication task (QDT) to similar FAQ selection tasks. The source domain is the wellknown English Quora dataset, while the target domain is a collection of small Italian datasets for real case scenarios consisting of FAQ groups retrieved by pivoting on common answers. Our results show great improvements in the zero-shot learning setting and modest improvements using the standard transfer approach for direct in-domain adaptation 1.
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