Tech Report Announcement

[Risto Miikkulainen]

[ Please send requests to valerie at cs.ucla.edu ]

	  A Modular Neural Network Architecture for 
	Sequential Paraphrasing of Script-Based Stories

	   Risto Miikkulainen  and  Michael G. Dyer
             Artificial Intelligence Laboratory
                 Computer Science Department
        University of California, Los Angeles, CA 90024

			  Abstract

We have applied sequential recurrent neural networks to a fairly
high-level cognitive task, i.e. paraphrasing script-based stories. Using
hierarchically organized modular subnetworks, which are trained
separately and in parallel, the complexity of the task is reduced by
effectively dividing it into subgoals.  The system uses sequential
natural language input and output, and develops its own I/O
representations for the words.  The representations are stored in an
external global lexicon, and they are adjusted in the course of training
by all four subnetworks simultaneously, according to the FGREP-method.
By concatenating a unique identification with the resulting
representation, an arbitrary number of instances of the same word type
can be created and used in the stories.  The system is able to produce a
fully expanded paraphrase of the story from only a few sentences, i.e.
the unmentioned events are inferred.  The word instances are correctly
bound to their roles, and simple plausible inferences of the variable
content of the story are made in the process.