31,344 research outputs found
Memory-augmented Neural Machine Translation
Neural machine translation (NMT) has achieved notable success in recent
times, however it is also widely recognized that this approach has limitations
with handling infrequent words and word pairs. This paper presents a novel
memory-augmented NMT (M-NMT) architecture, which stores knowledge about how
words (usually infrequently encountered ones) should be translated in a memory
and then utilizes them to assist the neural model. We use this memory mechanism
to combine the knowledge learned from a conventional statistical machine
translation system and the rules learned by an NMT system, and also propose a
solution for out-of-vocabulary (OOV) words based on this framework. Our
experiments on two Chinese-English translation tasks demonstrated that the
M-NMT architecture outperformed the NMT baseline by and BLEU points
on the two tasks, respectively. Additionally, we found this architecture
resulted in a much more effective OOV treatment compared to competitive
methods
On the Reproducibility and Generalisation of the Linear Transformation of Word Embeddings
Linear transformation is a way to learn a linear relationship between two word embeddings, such that words in the two different embedding spaces can be semantically related. In this paper, we examine the reproducibility and generalisation of the linear transformation of word embeddings. Linear transformation is particularly useful when translating word embedding models in different languages, since it can capture the semantic relationships between two models. We first reproduce two linear transformation approaches, a recent one using orthogonal transformation and the original one using simple matrix transformation. Previous findings on a machine translation task are re-examined, validating that linear transformation is indeed an effective way to transform word embedding models in different languages. In particular, we show that the orthogonal transformation can better relate the different embedding models. Following the verification of previous findings, we then study the generalisation of linear transformation in a multi-language Twitter election classification task. We observe that the orthogonal transformation outperforms the matrix transformation. In particular, it significantly outperforms the random classifier by at least 10% under the F1 metric across English and Spanish datasets. In addition, we also provide best practices when using linear transformation for multi-language Twitter election classification
A corpus-based survey of four electronic Swahili-English Bilingual dictionaries
In this article we survey four different electronic bilingual dictionaries for the language pair Swahili-English. Aided by a data-driven morphological analyzer and part-of-speech tagger, we quantify the coverage of the dictionaries on large monolingual corpora of Swahili. In a second series of experiments, we investigate how applicable the dictionaries are as a tool in the development of a machine translation system, by evaluating bilingual coverage on the parallel SAWA corpus. At the same time we attempt to consolidate the dictionaries into a unified lexicographic database and compare the coverage to that of its composite parts
Word-to-Word Models of Translational Equivalence
Parallel texts (bitexts) have properties that distinguish them from other
kinds of parallel data. First, most words translate to only one other word.
Second, bitext correspondence is noisy. This article presents methods for
biasing statistical translation models to reflect these properties. Analysis of
the expected behavior of these biases in the presence of sparse data predicts
that they will result in more accurate models. The prediction is confirmed by
evaluation with respect to a gold standard -- translation models that are
biased in this fashion are significantly more accurate than a baseline
knowledge-poor model. This article also shows how a statistical translation
model can take advantage of various kinds of pre-existing knowledge that might
be available about particular language pairs. Even the simplest kinds of
language-specific knowledge, such as the distinction between content words and
function words, is shown to reliably boost translation model performance on
some tasks. Statistical models that are informed by pre-existing knowledge
about the model domain combine the best of both the rationalist and empiricist
traditions
Identification of Fertile Translations in Medical Comparable Corpora: a Morpho-Compositional Approach
This paper defines a method for lexicon in the biomedical domain from
comparable corpora. The method is based on compositional translation and
exploits morpheme-level translation equivalences. It can generate translations
for a large variety of morphologically constructed words and can also generate
'fertile' translations. We show that fertile translations increase the overall
quality of the extracted lexicon for English to French translation
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