136 research outputs found
Three New Probabilistic Models for Dependency Parsing: An Exploration
After presenting a novel O(n^3) parsing algorithm for dependency grammar, we
develop three contrasting ways to stochasticize it. We propose (a) a lexical
affinity model where words struggle to modify each other, (b) a sense tagging
model where words fluctuate randomly in their selectional preferences, and (c)
a generative model where the speaker fleshes out each word's syntactic and
conceptual structure without regard to the implications for the hearer. We also
give preliminary empirical results from evaluating the three models' parsing
performance on annotated Wall Street Journal training text (derived from the
Penn Treebank). In these results, the generative (i.e., top-down) model
performs significantly better than the others, and does about equally well at
assigning part-of-speech tags.Comment: 6 pages, LaTeX 2.09 packaged with 4 .eps files, also uses colap.sty
and acl.bs
Hybrid example-based SMT: the best of both worlds?
(Way and Gough, 2005) provide an indepth comparison of their Example-Based Machine Translation (EBMT) system with
a Statistical Machine Translation (SMT) system constructed from freely available tools. According to a wide variety of automatic evaluation metrics, they demonstrated
that their EBMT system outperformed the SMT system by a factor of two to one.
Nevertheless, they did not test their EBMT system against a phrase-based SMT system. Obtaining their training and test
data for English–French, we carry out a number of experiments using the Pharaoh SMT Decoder. While better results are seen when Pharaoh is seeded with Giza++
word- and phrase-based data compared to EBMT sub-sentential alignments, in general better results are obtained when combinations of this 'hybrid' data is used to construct the translation and probability models. While for the most part the EBMT system of (Gough & Way, 2004b) outperforms any flavour of the phrasebased SMT systems constructed in our
experiments, combining the data sets automatically induced by both Giza++ and their EBMT system leads to a hybrid system which improves on the EBMT system per se for French–English
Exploiting multi-word units in history-based probabilistic generation
We present a simple history-based model for sentence generation from LFG f-structures, which improves on the accuracy of previous models by breaking down PCFG independence assumptions so that more f-structure conditioning context is used in the prediction of grammar rule expansions. In addition, we present work on experiments with named entities and other multi-word units,
showing a statistically significant improvement of generation accuracy. Tested on section 23 of the PennWall Street Journal Treebank, the techniques described in this paper improve BLEU scores from 66.52 to 68.82, and coverage from 98.18% to 99.96%
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