18 research outputs found
Natural Language Processing with Small Feed-Forward Networks
We show that small and shallow feed-forward neural networks can achieve near
state-of-the-art results on a range of unstructured and structured language
processing tasks while being considerably cheaper in memory and computational
requirements than deep recurrent models. Motivated by resource-constrained
environments like mobile phones, we showcase simple techniques for obtaining
such small neural network models, and investigate different tradeoffs when
deciding how to allocate a small memory budget.Comment: EMNLP 2017 short pape
Source side pre-ordering using recurrent neural networks for English-Myanmar machine translation
Word reordering has remained one of the challenging problems for machine translation when translating between language pairs with different word orders e.g. English and Myanmar. Without reordering between these languages, a source sentence may be translated directly with similar word order and translation can not be meaningful. Myanmar is a subject-objectverb (SOV) language and an effective reordering is essential for translation. In this paper, we applied a pre-ordering approach using recurrent neural networks to pre-order words of the source Myanmar sentence into target English’s word order. This neural pre-ordering model is automatically derived from parallel word-aligned data with syntactic and lexical features based on dependency parse trees of the source sentences. This can generate arbitrary permutations that may be non-local on the sentence and can be combined into English-Myanmar machine translation. We exploited the model to reorder English sentences into Myanmar-like word order as a preprocessing stage for machine translation, obtaining improvements quality comparable to baseline rule-based pre-ordering approach on asian language treebank (ALT) corpus
Improving fast align by reordering,”
Abstract fast align is a simple, fast, and efficient approach for word alignment based on the IBM model 2. fast align performs well for language pairs with relatively similar word orders; however, it does not perform well for language pairs with drastically different word orders. We propose a segmenting-reversing reordering process to solve this problem by alternately applying fast align and reordering source sentences during training. Experimental results with JapaneseEnglish translation demonstrate that the proposed approach improves the performance of fast align significantly without the loss of efficiency. Experiments using other languages are also reported
Linguistic Structure in Statistical Machine Translation
This thesis investigates the influence of linguistic structure in statistical machine translation. We develop a word reordering model based on syntactic parse trees and address the issues of pronouns and morphological agreement with a source discriminative word lexicon predicting the translation for individual words using structural features. When used in phrase-based machine translation, the models improve the translation for language pairs with different word order and morphological variation
The QT21/HimL Combined Machine Translation System
This paper describes the joint submission
of the QT21 and HimL projects for
the English→Romanian translation task of
the ACL 2016 First Conference on Machine
Translation (WMT 2016). The submission
is a system combination which
combines twelve different statistical machine
translation systems provided by the
different groups (RWTH Aachen University,
LMU Munich, Charles University in
Prague, University of Edinburgh, University
of Sheffield, Karlsruhe Institute of
Technology, LIMSI, University of Amsterdam,
Tilde). The systems are combined
using RWTH’s system combination
approach. The final submission shows an
improvement of 1.0 BLEU compared to the
best single system on newstest2016