Meichun Liu

The Module-Attribute Representation of Verbal Semantics: From Semantics to Argument Structure

In this paper, we set forth a theory of lexical knowledge. We propose two types of modules: event structure modules and role modules, as well as two sets of attributes: event-internal attributes and role-internal attributes, which are linked to the event structure module and role module, respectively. These module-attribute semantic representations have associated grammatical consequences. Our data is drawn from a comprehensive corpus-based study of Mandarin Chinese verbal semantics, and four particular case studies are presented.

SWIM: A Simple Word Interaction Model for Implicit Discourse Relation Recognition

Capturing the semantic interaction of pairs of words across arguments and proper argument representation are both crucial issues in implicit discourse relation recognition. The current state-of-the-art represents arguments as distributional vectors that are computed via bi-directional Long Short-Term Memory networks (BiLSTMs), known to have significant model complexity. In contrast, we demonstrate that word-weighted averaging can encode argument representation which can be incorporated with word pair information efficiently. By saving an order of magnitude in parameters and eschewing the recurrent structure, our proposed model achieves equivalent performance, but trains seven times faster.