411 research outputs found
Learning to Resolve Natural Language Ambiguities: A Unified Approach
We analyze a few of the commonly used statistics based and machine learning
algorithms for natural language disambiguation tasks and observe that they can
be re-cast as learning linear separators in the feature space. Each of the
methods makes a priori assumptions, which it employs, given the data, when
searching for its hypothesis. Nevertheless, as we show, it searches a space
that is as rich as the space of all linear separators. We use this to build an
argument for a data driven approach which merely searches for a good linear
separator in the feature space, without further assumptions on the domain or a
specific problem.
We present such an approach - a sparse network of linear separators,
utilizing the Winnow learning algorithm - and show how to use it in a variety
of ambiguity resolution problems. The learning approach presented is
attribute-efficient and, therefore, appropriate for domains having very large
number of attributes.
In particular, we present an extensive experimental comparison of our
approach with other methods on several well studied lexical disambiguation
tasks such as context-sensitive spelling correction, prepositional phrase
attachment and part of speech tagging. In all cases we show that our approach
either outperforms other methods tried for these tasks or performs comparably
to the best
A Winnow-Based Approach to Context-Sensitive Spelling Correction
A large class of machine-learning problems in natural language require the
characterization of linguistic context. Two characteristic properties of such
problems are that their feature space is of very high dimensionality, and their
target concepts refer to only a small subset of the features in the space.
Under such conditions, multiplicative weight-update algorithms such as Winnow
have been shown to have exceptionally good theoretical properties. We present
an algorithm combining variants of Winnow and weighted-majority voting, and
apply it to a problem in the aforementioned class: context-sensitive spelling
correction. This is the task of fixing spelling errors that happen to result in
valid words, such as substituting "to" for "too", "casual" for "causal", etc.
We evaluate our algorithm, WinSpell, by comparing it against BaySpell, a
statistics-based method representing the state of the art for this task. We
find: (1) When run with a full (unpruned) set of features, WinSpell achieves
accuracies significantly higher than BaySpell was able to achieve in either the
pruned or unpruned condition; (2) When compared with other systems in the
literature, WinSpell exhibits the highest performance; (3) The primary reason
that WinSpell outperforms BaySpell is that WinSpell learns a better linear
separator; (4) When run on a test set drawn from a different corpus than the
training set was drawn from, WinSpell is better able than BaySpell to adapt,
using a strategy we will present that combines supervised learning on the
training set with unsupervised learning on the (noisy) test set.Comment: To appear in Machine Learning, Special Issue on Natural Language
Learning, 1999. 25 page
An empirical study of Conv-TasNet
Conv-TasNet is a recently proposed waveform-based deep neural network that
achieves state-of-the-art performance in speech source separation. Its
architecture consists of a learnable encoder/decoder and a separator that
operates on top of this learned space. Various improvements have been proposed
to Conv-TasNet. However, they mostly focus on the separator, leaving its
encoder/decoder as a (shallow) linear operator. In this paper, we conduct an
empirical study of Conv-TasNet and propose an enhancement to the
encoder/decoder that is based on a (deep) non-linear variant of it. In
addition, we experiment with the larger and more diverse LibriTTS dataset and
investigate the generalization capabilities of the studied models when trained
on a much larger dataset. We propose cross-dataset evaluation that includes
assessing separations from the WSJ0-2mix, LibriTTS and VCTK databases. Our
results show that enhancements to the encoder/decoder can improve average
SI-SNR performance by more than 1 dB. Furthermore, we offer insights into the
generalization capabilities of Conv-TasNet and the potential value of
improvements to the encoder/decoder.Comment: In proceedings of ICASSP202
Multi-engine machine translation by recursive sentence decomposition
In this paper, we present a novel approach to combine the outputs of multiple MT engines into a consensus translation. In contrast to previous Multi-Engine Machine
Translation (MEMT) techniques, we do not rely on word alignments of output hypotheses, but prepare the input sentence for multi-engine processing. We do this by using a recursive decomposition algorithm that produces simple chunks as input to the MT engines. A consensus translation
is produced by combining the best chunk translations, selected through majority voting, a trigram language model
score and a confidence score assigned to each MT engine. We report statistically significant relative improvements
of up to 9% BLEU score in experiments (English→Spanish) carried out on an 800-sentence test set extracted from the Penn-II Treebank
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