A Minimum Message Length Approach for Discourse Interpretation

用自然语言与计算机系统通信的能力具有巨大的潜力，可以改善用户的整体体验。然而，目前的系统仅支持有限的通信手段。在这个项目中，我们打算研究模型选择技术在解释人类语篇中的应用。特别是，我们将考虑计算机在其自身知识的背景下解释用户话语的情况。我们方法的多功能性将通过使用它来展示：(1)在人机对话中解释话语，(2)为短文提供反馈，以及(3)确定文档对模型的影响。

The ability to communicate with computer systems in Natural Language has great potential to improve the overall experience for users. However, current systems support only limited means of communication. In this project, we propose to investigate the application of model-selection techniques for interpreting human discourse. In particular, we will consider situations where the computer interprets users' discourse in the context of its own knowledge. The versatility of our approach will be demonstrated by using it to (1) interpret discourse in a human-computer dialogue, (2) provide feedback to short essays, and (3) determine the impact of a document on a model.