3,225 research outputs found
Inducing Probabilistic Grammars by Bayesian Model Merging
We describe a framework for inducing probabilistic grammars from corpora of
positive samples. First, samples are {\em incorporated} by adding ad-hoc rules
to a working grammar; subsequently, elements of the model (such as states or
nonterminals) are {\em merged} to achieve generalization and a more compact
representation. The choice of what to merge and when to stop is governed by the
Bayesian posterior probability of the grammar given the data, which formalizes
a trade-off between a close fit to the data and a default preference for
simpler models (`Occam's Razor'). The general scheme is illustrated using three
types of probabilistic grammars: Hidden Markov models, class-based -grams,
and stochastic context-free grammars.Comment: To appear in Grammatical Inference and Applications, Second
International Colloquium on Grammatical Inference; Springer Verlag, 1994. 13
page
Introduction to the CoNLL-2000 Shared Task: Chunking
We describe the CoNLL-2000 shared task: dividing text into syntactically
related non-overlapping groups of words, so-called text chunking. We give
background information on the data sets, present a general overview of the
systems that have taken part in the shared task and briefly discuss their
performance.Comment: 6 page
Translating Phrases in Neural Machine Translation
Phrases play an important role in natural language understanding and machine
translation (Sag et al., 2002; Villavicencio et al., 2005). However, it is
difficult to integrate them into current neural machine translation (NMT) which
reads and generates sentences word by word. In this work, we propose a method
to translate phrases in NMT by integrating a phrase memory storing target
phrases from a phrase-based statistical machine translation (SMT) system into
the encoder-decoder architecture of NMT. At each decoding step, the phrase
memory is first re-written by the SMT model, which dynamically generates
relevant target phrases with contextual information provided by the NMT model.
Then the proposed model reads the phrase memory to make probability estimations
for all phrases in the phrase memory. If phrase generation is carried on, the
NMT decoder selects an appropriate phrase from the memory to perform phrase
translation and updates its decoding state by consuming the words in the
selected phrase. Otherwise, the NMT decoder generates a word from the
vocabulary as the general NMT decoder does. Experiment results on the Chinese
to English translation show that the proposed model achieves significant
improvements over the baseline on various test sets.Comment: Accepted by EMNLP 201
Statistical and Computational Tradeoffs in Stochastic Composite Likelihood
Maximum likelihood estimators are often of limited practical use due to the
intensive computation they require. We propose a family of alternative
estimators that maximize a stochastic variation of the composite likelihood
function. Each of the estimators resolve the computation-accuracy tradeoff
differently, and taken together they span a continuous spectrum of
computation-accuracy tradeoff resolutions. We prove the consistency of the
estimators, provide formulas for their asymptotic variance, statistical
robustness, and computational complexity. We discuss experimental results in
the context of Boltzmann machines and conditional random fields. The
theoretical and experimental studies demonstrate the effectiveness of the
estimators when the computational resources are insufficient. They also
demonstrate that in some cases reduced computational complexity is associated
with robustness thereby increasing statistical accuracy.Comment: 30 pages, 97 figures, 2 author
Distribution matching for transduction
Many transductive inference algorithms assume that distributions over training and test estimates should be related, e.g. by providing a large margin of separation on both sets. We use this idea to design a transduction algorithm which can be used without modification for classification, regression, and structured estimation. At its heart we exploit the fact that for a good learner the distributions over the outputs on training and test sets should match. This is a classical two-sample problem which can be solved efficiently in its most general form by using distance measures in Hilbert Space. It turns out that a number of existing heuristics can be viewed as special cases of our approach.
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