6,591 research outputs found
An Application of Probabilistic Grammars to Efficient Machne Translation
In this paper we present one of the algorithms used to parse probabilistic context-free grammars: the A* parsing algorithm, which is based on the A* graph search method. We show an example of application of the algorithm in an existing machine translation system. The existing CYK-based parser used in the Translatica system was modified by applying the A* parsing algorithm in order to examine the possibilities of improving its performance. This paper presents the results of applying the A* algorithm with different heuristic functions and their impact on the performance of the parser
A discriminative latent variable-based "DE" classifier for ChineseâEnglish SMT
Syntactic reordering on the source-side
is an effective way of handling word order
differences. The (DE) construction
is a flexible and ubiquitous syntactic
structure in Chinese which is a major
source of error in translation quality.
In this paper, we propose a new classifier
model â discriminative latent variable
model (DPLVM) â to classify the
DE construction to improve the accuracy
of the classification and hence the translation
quality. We also propose a new feature
which can automatically learn the reordering
rules to a certain extent. The experimental
results show that the MT systems
using the data reordered by our proposed
model outperform the baseline systems
by 6.42% and 3.08% relative points
in terms of the BLEU score on PB-SMT
and hierarchical phrase-based MT respectively.
In addition, we analyse the impact
of DE annotation on word alignment and
on the SMT phrase table
AMR Dependency Parsing with a Typed Semantic Algebra
We present a semantic parser for Abstract Meaning Representations which
learns to parse strings into tree representations of the compositional
structure of an AMR graph. This allows us to use standard neural techniques for
supertagging and dependency tree parsing, constrained by a linguistically
principled type system. We present two approximative decoding algorithms, which
achieve state-of-the-art accuracy and outperform strong baselines.Comment: This paper will be presented at ACL 2018 (see
https://acl2018.org/programme/papers/
Global Thresholding and Multiple Pass Parsing
We present a variation on classic beam thresholding techniques that is up to
an order of magnitude faster than the traditional method, at the same
performance level. We also present a new thresholding technique, global
thresholding, which, combined with the new beam thresholding, gives an
additional factor of two improvement, and a novel technique, multiple pass
parsing, that can be combined with the others to yield yet another 50%
improvement. We use a new search algorithm to simultaneously optimize the
thresholding parameters of the various algorithms.Comment: Fixed latex errors; fixed minor errors in published versio
- âŠ