The OSU Realizer for SRST ‘18: Neural Sequence-to-Sequence Inflection and Incremental Locality-Based Linearization

David King, Michael White


Abstract
Surface realization is a nontrivial task as it involves taking structured data and producing grammatically and semantically correct utterances. Many competing grammar-based and statistical models for realization still struggle with relatively simple sentences. For our submission to the 2018 Surface Realization Shared Task, we tackle the shallow task by first generating inflected wordforms with a neural sequence-to-sequence model before incrementally linearizing them. For linearization, we use a global linear model trained using early update that makes use of features that take into account the dependency structure and dependency locality. Using this pipeline sufficed to produce surprisingly strong results in the shared task. In future work, we intend to pursue joint approaches to linearization and morphological inflection and incorporating a neural language model into the linearization choices.
Anthology ID:
W18-3605
Volume:
Proceedings of the First Workshop on Multilingual Surface Realisation
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
39–48
Language:
URL:
https://aclanthology.org/W18-3605
DOI:
10.18653/v1/W18-3605
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/W18-3605.pdf