Learning Feature-Based Semantics with Simple Recurrent Networks
Title | Learning Feature-Based Semantics with Simple Recurrent Networks |
Publication Type | Technical Report |
Year of Publication | 1990 |
Authors | Stolcke, A. |
Other Numbers | 579 |
Abstract | The paper investigates the possibilities for using simple recurrent networks as transducers which map sequential natural language input into non-sequential feature-based semantics. The networks perform well on sentences containing a single main predicate (encoded by transitive verbs or prepositions) applied to multiple-feature objects (encoded as noun-phrases with adjectival modifiers), and shows robustness against ungrammatical inputs. A second set of experiments deals with sentences containing embedded structures. Here the network is able to process multiple levels of sentence-final embeddings but only one level of center-embedding. This turns out to be a consequence of the network's inability to retain information that is not reflected in the outputs over intermediate phases of processing. Two extensions to Elman's shortcite{Elman:88} original recurrent network architecture are introduced. |
URL | http://www.icsi.berkeley.edu/ftp/global/pub/techreports/1990/tr-90-015.pdf |
Bibliographic Notes | ICSI Technical Report TR-90-015 |
Abbreviated Authors | A. Stolcke |
ICSI Publication Type | Technical Report |