71 research outputs found
Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction
Labeled sequence transduction is a task of transforming one sequence into
another sequence that satisfies desiderata specified by a set of labels. In
this paper we propose multi-space variational encoder-decoders, a new model for
labeled sequence transduction with semi-supervised learning. The generative
model can use neural networks to handle both discrete and continuous latent
variables to exploit various features of data. Experiments show that our model
provides not only a powerful supervised framework but also can effectively take
advantage of the unlabeled data. On the SIGMORPHON morphological inflection
benchmark, our model outperforms single-model state-of-art results by a large
margin for the majority of languages.Comment: Accepted by ACL 201
Density Matching for Bilingual Word Embedding
Recent approaches to cross-lingual word embedding have generally been based
on linear transformations between the sets of embedding vectors in the two
languages. In this paper, we propose an approach that instead expresses the two
monolingual embedding spaces as probability densities defined by a Gaussian
mixture model, and matches the two densities using a method called normalizing
flow. The method requires no explicit supervision, and can be learned with only
a seed dictionary of words that have identical strings. We argue that this
formulation has several intuitively attractive properties, particularly with
the respect to improving robustness and generalization to mappings between
difficult language pairs or word pairs. On a benchmark data set of bilingual
lexicon induction and cross-lingual word similarity, our approach can achieve
competitive or superior performance compared to state-of-the-art published
results, with particularly strong results being found on etymologically distant
and/or morphologically rich languages.Comment: Accepted by NAACL-HLT 201
Look-back Decoding for Open-Ended Text Generation
Given a prefix (context), open-ended generation aims to decode texts that are
coherent, which don't abruptly drift from previous topics, and informative,
which don't suffer from undesired repetitions. In this paper, we propose
Look-back, an improved decoding algorithm that leverages the Kullback-Leibler
divergence to track the distribution distance between current and historical
decoding steps. Thus Look-back can automatically predict potential repetitive
phrase and topic drift, and remove tokens that may cause the failure modes,
restricting the next token probability distribution within a plausible distance
to the history. We perform decoding experiments on document continuation and
story generation, and demonstrate that Look-back is able to generate more
fluent and coherent text, outperforming other strong decoding methods
significantly in both automatic and human evaluations
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