Efficient Inference and Structured Learning for Semantic Role Labeling

Oscar Täckström, Kuzman Ganchev, Dipanjan Das


Abstract
We present a dynamic programming algorithm for efficient constrained inference in semantic role labeling. The algorithm tractably captures a majority of the structural constraints examined by prior work in this area, which has resorted to either approximate methods or off-the-shelf integer linear programming solvers. In addition, it allows training a globally-normalized log-linear model with respect to constrained conditional likelihood. We show that the dynamic program is several times faster than an off-the-shelf integer linear programming solver, while reaching the same solution. Furthermore, we show that our structured model results in significant improvements over its local counterpart, achieving state-of-the-art results on both PropBank- and FrameNet-annotated corpora.
Anthology ID:
Q15-1003
Volume:
Transactions of the Association for Computational Linguistics, Volume 3
Month:
Year:
2015
Address:
Venue:
TACL
SIG:
Publisher:
Note:
Pages:
29–41
Language:
URL:
https://www.aclweb.org/anthology/Q15-1003
DOI:
10.1162/tacl_a_00120
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PDF:
http://aclanthology.lst.uni-saarland.de/Q15-1003.pdf
Video:
 https://techtalks.tv/talks/efficient-inference-and-structured-learning-for-semantic-role-labeling/61788/