13,506 research outputs found
OpenMaTrEx: a free/open-source marker-driven example-based machine translation system
We describe OpenMaTrEx, a free/open-source example based
machine translation (EBMT) system based on the marker hypothesis, comprising a marker-driven chunker, a collection of chunk aligners, and two engines: one based on a simple proof-of-concept monotone EBMT recombinator and a Moses-based statistical decoder. OpenMaTrEx is a free/open-source release of the basic components of MaTrEx, the Dublin City University machine translation system
Chunk-Based Bi-Scale Decoder for Neural Machine Translation
In typical neural machine translation~(NMT), the decoder generates a sentence
word by word, packing all linguistic granularities in the same time-scale of
RNN. In this paper, we propose a new type of decoder for NMT, which splits the
decode state into two parts and updates them in two different time-scales.
Specifically, we first predict a chunk time-scale state for phrasal modeling,
on top of which multiple word time-scale states are generated. In this way, the
target sentence is translated hierarchically from chunks to words, with
information in different granularities being leveraged. Experiments show that
our proposed model significantly improves the translation performance over the
state-of-the-art NMT model.Comment: Accepted as a short paper by ACL 201
Combining semantic and syntactic generalization in example-based machine translation
In this paper, we report our experiments in combining two EBMT systems that rely on generalized templates, Marclator and CMU-EBMT, on an English–German translation task. Our goal was to see whether a statistically significant improvement could be achieved over the individual performances of these two systems. We observed that this was not the case. However, our system consistently outperformed a lexical EBMT baseline system
Example-based machine translation of the Basque language
Basque is both a minority and a highly inflected language with free order of sentence constituents. Machine Translation of Basque is thus both a real need and a test bed for MT techniques. In this paper, we present a modular Data-Driven MT system which includes different chunkers as well as chunk aligners which can deal with the free order of sentence constituents of Basque. We conducted Basque to English translation experiments, evaluated on a large corpus
(270, 000 sentence pairs). The experimental results show that our system significantly outperforms state-of-the-art
approaches according to several common automatic evaluation metrics
A memory-based classification approach to marker-based EBMT
We describe a novel approach to example-based machine translation that makes use of marker-based chunks, in which the decoder is a memory-based classifier. The classifier is trained to map trigrams of source-language chunks onto trigrams of target-language chunks; then, in a second
decoding step, the predicted trigrams are rearranged according to their overlap. We present the first results of this method on a Dutch-to-English translation system
using Europarl data. Sparseness of the class space causes the results to lag behind a baseline phrase-based SMT system.
In a further comparison, we also
apply the method to a word-aligned version
of the same data, and report a smaller
difference with a word-based SMT system.
We explore the scaling abilities of the
memory-based approach, and observe linear
scaling behavior in training and classification
speed and memory costs, and loglinear
BLEU improvements in the amount
of training examples
Hybrid rule-based - example-based MT: feeding apertium with sub-sentential translation units
This paper describes a hybrid machine translation (MT) approach that consists of integrating bilingual chunks (sub-sentential translation units) obtained from parallel corpora into an MT system built using the Apertium free/open-source rule-based machine translation platform, which uses a shallow-transfer translation approach. In the integration of bilingual chunks, special care has been
taken so as not to break the application of the existing Apertium structural transfer rules, since this would increase the number of ungrammatical translations. The method consists of (i) the application of a dynamic-programming algorithm to compute the best translation coverage of the input sentence given the collection of bilingual chunks available; (ii) the translation of the input sentence as usual by Apertium; and (iii) the application of a language model to choose one of the possible translations for each of the bilingual chunks detected. Results are reported for the translation from English-to-Spanish, and vice versa, when marker-based bilingual chunks automatically obtained from parallel
corpora are used
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
Wrapper syntax for example-based machine translation
TransBooster is a wrapper technology designed to improve the performance of wide-coverage machine translation
systems. Using linguistically motivated syntactic information, it automatically decomposes source language sentences into shorter and syntactically simpler chunks, and recomposes their translation to form target language sentences. This generally improves both the word order
and lexical selection of the translation. To date, TransBooster has been successfully applied to rule-based MT, statistical MT, and multi-engine MT. This paper presents
the application of TransBooster to Example-Based Machine Translation. In an experiment conducted on test sets
extracted from Europarl and the Penn II Treebank we show that our method can raise the BLEU score up to 3.8% relative
to the EBMT baseline. We also conduct a manual evaluation, showing that TransBooster-enhanced EBMT produces
a better output in terms of fluency than the baseline EBMT in 55% of the cases and in terms of accuracy in 53% of the
cases
MATREX: DCU machine translation system for IWSLT 2006
In this paper, we give a description of the machine translation system developed at DCU that was used for our first participation in the evaluation campaign of the International Workshop on Spoken Language Translation (2006). This system combines two types of approaches. First, we use an EBMT approach to collect aligned chunks based on two steps: deterministic chunking of both sides and chunk alignment. We use several chunking and alignment strategies. We also extract SMT-style aligned phrases, and the two types of resources are combined.
We participated in the Open Data Track for the following
translation directions: Arabic-English and Italian-English,
for which we translated both the single-best ASR hypotheses
and the text input. We report the results of the system for
the provided evaluation sets
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