电子邮件承诺检测的领域自适应

H. Azarbonyad, Robert Sim, Ryen W. White
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引用次数: 11

摘要

人们经常会对未来的行动做出承诺。检测电子邮件中的承诺(例如,“我会在今天结束前发送报告”)使数字助理能够帮助用户回忆起他们所做的承诺,并帮助他们及时实现这些承诺。在本文中,我们表明,当模型在同一领域(语料库)上进行训练和评估时,可以可靠地从电子邮件中提取承诺。然而,当评估域不同时,它们的性能会下降。这说明了与电子邮件数据集相关的领域偏差,以及对更健壮和可推广的承诺检测模型的需求。为了学习一个独立于领域的承诺模型,我们首先对领域(电子邮件语料库)之间的差异进行表征,然后使用这种表征在它们之间转移知识。我们研究了不同粒度的域适应,即迁移学习的性能:特征级适应和样本级适应。我们使用一个神经自编码器来学习训练样本的领域独立表示,进一步扩展了这一点。我们表明迁移学习可以帮助消除领域偏见,以获得较少的领域依赖的模型。总体而言,我们的研究结果表明,领域差异会对承诺检测模型的质量产生显著的负面影响,迁移学习在解决这一问题方面具有巨大的潜力。
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Domain Adaptation for Commitment Detection in Email
People often make commitments to perform future actions. Detecting commitments made in email (e.g., "I'll send the report by end of day'') enables digital assistants to help their users recall promises they have made and assist them in meeting those promises in a timely manner. In this paper, we show that commitments can be reliably extracted from emails when models are trained and evaluated on the same domain (corpus). However, their performance degrades when the evaluation domain differs. This illustrates the domain bias associated with email datasets and a need for more robust and generalizable models for commitment detection. To learn a domain-independent commitment model, we first characterize the differences between domains (email corpora) and then use this characterization to transfer knowledge between them. We investigate the performance of domain adaptation, namely transfer learning, at different granularities: feature-level adaptation and sample-level adaptation. We extend this further using a neural autoencoder trained to learn a domain-independent representation for training samples. We show that transfer learning can help remove domain bias to obtain models with less domain dependence. Overall, our results show that domain differences can have a significant negative impact on the quality of commitment detection models and that transfer learning has enormous potential to address this issue.
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