Applying Machine Learning to Discourse Processing
Papers from the AAAI Spring Symposium
Jennifer Chu-Carroll and Nancy Green,Cochairs
Following success in using machine learning (ML) techniques in speech, syntactic, and semantic processing, there has been an increasing interest in applying ML to discourse problems such as dialogue act prediction, cue word usage, and discourse segmentation. This symposium will bring together researchers interested in exploring the potential contribution of ML to discourse interpretation and generation to address the following issues:
From the discourse processing point of view, issues include: What tasks in discourse understanding / generation are most suitable for processing using ML-acquired models? And why? Which ML approaches successfully adopted by other areas of natural language processing seem promising for use in discourse processing? And why? How can learning be performed during the discourse comprehension or generation process? How can knowledge acquired for discourse interpretation or generation be reused for the other? What types of pragmatic knowledge (e.g., discourse recipes, cue phrase classification) can be acquired by ML? What kinds of categories and features can be tagged automatically and/or reliably? How can useful features be identified?
From the machine learning point of view, issues include: What are the different ML techniques that may be suitable for acquiring knowledge for discourse processing? What are the features of these ML techniques that make them particularly suitable for application in discourse processing? How does the performance (e.g., accuracy, processing speed) of models for discourse processing based on ML techniques compare to those based on traditional methods? How do different ML techniques compare with one another in terms of accuracy, efficiency, amount of data needed for training, etc., for various problems in discourse processing? What discourse corpora are currently available for ML? What other corpora are needed for ML research? What characteristics of discourse processing cause problems for existing ML techniques?