141 research outputs found
Stochastic Collapsed Variational Inference for Sequential Data
Stochastic variational inference for collapsed models has recently been
successfully applied to large scale topic modelling. In this paper, we propose
a stochastic collapsed variational inference algorithm in the sequential data
setting. Our algorithm is applicable to both finite hidden Markov models and
hierarchical Dirichlet process hidden Markov models, and to any datasets
generated by emission distributions in the exponential family. Our experiment
results on two discrete datasets show that our inference is both more efficient
and more accurate than its uncollapsed version, stochastic variational
inference.Comment: NIPS Workshop on Advances in Approximate Bayesian Inference, 201
Modelling the Lexicon in Unsupervised Part of Speech Induction
Automatically inducing the syntactic part-of-speech categories for words in
text is a fundamental task in Computational Linguistics. While the performance
of unsupervised tagging models has been slowly improving, current
state-of-the-art systems make the obviously incorrect assumption that all
tokens of a given word type must share a single part-of-speech tag. This
one-tag-per-type heuristic counters the tendency of Hidden Markov Model based
taggers to over generate tags for a given word type. However, it is clearly
incompatible with basic syntactic theory. In this paper we extend a
state-of-the-art Pitman-Yor Hidden Markov Model tagger with an explicit model
of the lexicon. In doing so we are able to incorporate a soft bias towards
inducing few tags per type. We develop a particle filter for drawing samples
from the posterior of our model and present empirical results that show that
our model is competitive with and faster than the state-of-the-art without
making any unrealistic restrictions.Comment: To be presented at the 14th Conference of the European Chapter of the
Association for Computational Linguistic
Language as a Latent Variable: Discrete Generative Models for Sentence Compression
In this work we explore deep generative models of text in which the latent
representation of a document is itself drawn from a discrete language model
distribution. We formulate a variational auto-encoder for inference in this
model and apply it to the task of compressing sentences. In this application
the generative model first draws a latent summary sentence from a background
language model, and then subsequently draws the observed sentence conditioned
on this latent summary. In our empirical evaluation we show that generative
formulations of both abstractive and extractive compression yield
state-of-the-art results when trained on a large amount of supervised data.
Further, we explore semi-supervised compression scenarios where we show that it
is possible to achieve performance competitive with previously proposed
supervised models while training on a fraction of the supervised data.Comment: EMNLP 201
Multilingual Models for Compositional Distributed Semantics
We present a novel technique for learning semantic representations, which
extends the distributional hypothesis to multilingual data and joint-space
embeddings. Our models leverage parallel data and learn to strongly align the
embeddings of semantically equivalent sentences, while maintaining sufficient
distance between those of dissimilar sentences. The models do not rely on word
alignments or any syntactic information and are successfully applied to a
number of diverse languages. We extend our approach to learn semantic
representations at the document level, too. We evaluate these models on two
cross-lingual document classification tasks, outperforming the prior state of
the art. Through qualitative analysis and the study of pivoting effects we
demonstrate that our representations are semantically plausible and can capture
semantic relationships across languages without parallel data.Comment: Proceedings of ACL 2014 (Long papers
Compositional Morphology for Word Representations and Language Modelling
This paper presents a scalable method for integrating compositional
morphological representations into a vector-based probabilistic language model.
Our approach is evaluated in the context of log-bilinear language models,
rendered suitably efficient for implementation inside a machine translation
decoder by factoring the vocabulary. We perform both intrinsic and extrinsic
evaluations, presenting results on a range of languages which demonstrate that
our model learns morphological representations that both perform well on word
similarity tasks and lead to substantial reductions in perplexity. When used
for translation into morphologically rich languages with large vocabularies,
our models obtain improvements of up to 1.2 BLEU points relative to a baseline
system using back-off n-gram models.Comment: Proceedings of the 31st International Conference on Machine Learning
(ICML
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