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.