Wang Ling

In this talk, I will describe our work on character-based models for natural language processing. This talk will be divided in two parts. In the first part, I will describe our previous work on character-based word representation, where the syntax and semantic of word level units are leanly solely from the character-level. We show that these models can automatically learn morphologically oriented features that would be difficult to discover manually as features and show significant improvements in many natural language processing tasks. In the second part of the talk, I shall describe our ongoing work on character-based language generation and word segmentation/discovery. We show that the challenges involved in building character-level generation models, and then describe our incremental research contributions towards models that can automatically generate language but also jointly learn word segments.