Emails in the workplace are often intentional calls to action for its recipients. We propose to annotate these emails for what action its recipient will take. We argue that our approach of action-based annotation is more scalable and theory-agnostic than traditional speech-act-based email intent annotation, while still carrying important semantic and pragmatic information. We show that our action-based annotation scheme achieves good inter-annotator agreement. We also show that we can leverage threaded messages from other domains, which exhibit comparable intents in their conversation, with domain adaptive RAINBOW (Recurrently AttentIve Neural Bag-Of-Words). On a collection of datasets consisting of IRC, Reddit, and email, our reparametrized RNNs outperform common multitask multidomain approaches on several speech act related tasks. We also experiment with a minimally supervised scenario of email recipient action classification, and find the reparametrized RNNs learn a useful representation.
There has been a lot of work on multidomain task learning with shared representation as we described in sec:introduction . Our work is also closely related to work on email speech act modeling and recognition @cite_17 @cite_23 @cite_6 @cite_24 . The idea of model reparametrization for domain adaption is abundant in the literature of hierarchical Bayesian modeling, such as .
@cite_24 @cite_6 @cite_23 @cite_17
2019169797 2089285937 35388109
This study proposes an approach to automatically score the TOEIC® Writing e-mail task. We focus on one component of the scoring rubric, which notes whether the test-takers have used particular speech acts such as requests, orders, or commitments. We developed a computational model for automated speech act identification and tested it on a corpus of TOEIC responses, achieving up to 79.28 accuracy. This model represents a positive first step toward the development of a more comprehensive scoring model. We also created a corpus of speech act-annotated native English workplace e-mails. Comparisons between these and the TOEIC data allow us to assess whether English learners are approximating native models and whether differences between native and non-native data can have negative consequences in the global workplace. In this paper, we present a semi-supervised method for automatic speech act recognition in email and forums. The major challenge of this task is due to lack of labeled data in these two genres. Our method leverages labeled data in the Switchboard-DAMSL and the Meeting Recorder Dialog Act database and applies simple domain adaptation techniques over a large amount of unlabeled email and forum data to address this problem. Our method uses automatically extracted features such as phrases and dependency trees, called subtree features, for semi-supervised learning. Empirical results demonstrate that our model is effective in email and forum speech act recognition. It is often useful to classify email according to the intent of the sender (e.g., "propose a meeting", "deliver information"). We present experimental results in learning to classify email in this fashion, where each class corresponds to a verbnoun pair taken from a predefined ontology describing typical “email speech acts”. We demonstrate that, although this categorization problem is quite different from “topical” text classification, certain categories of messages can nonetheless be detected with high precision (above 80 ) and reasonable recall (above 50 ) using existing text-classification learning methods. This result suggests that useful task-tracking tools could be constructed based on automatic classification into this taxonomy.