13,998 research outputs found
Robust Tuning Datasets for Statistical Machine Translation
We explore the idea of automatically crafting a tuning dataset for
Statistical Machine Translation (SMT) that makes the hyper-parameters of the
SMT system more robust with respect to some specific deficiencies of the
parameter tuning algorithms. This is an under-explored research direction,
which can allow better parameter tuning. In this paper, we achieve this goal by
selecting a subset of the available sentence pairs, which are more suitable for
specific combinations of optimizers, objective functions, and evaluation
measures. We demonstrate the potential of the idea with the pairwise ranking
optimization (PRO) optimizer, which is known to yield too short translations.
We show that the learning problem can be alleviated by tuning on a subset of
the development set, selected based on sentence length. In particular, using
the longest 50% of the tuning sentences, we achieve two-fold tuning speedup,
and improvements in BLEU score that rival those of alternatives, which fix
BLEU+1's smoothing instead.Comment: RANLP-201
Trivial Transfer Learning for Low-Resource Neural Machine Translation
Transfer learning has been proven as an effective technique for neural
machine translation under low-resource conditions. Existing methods require a
common target language, language relatedness, or specific training tricks and
regimes. We present a simple transfer learning method, where we first train a
"parent" model for a high-resource language pair and then continue the training
on a lowresource pair only by replacing the training corpus. This "child" model
performs significantly better than the baseline trained for lowresource pair
only. We are the first to show this for targeting different languages, and we
observe the improvements even for unrelated languages with different alphabets.Comment: Accepted to WMT18 reseach paper, Proceedings of the 3rd Conference on
Machine Translation 201
Experiments on domain adaptation for English-Hindi SMT
Statistical Machine Translation (SMT) systems are usually trained on large amounts of bilingual text and monolingual target language text. If a significant amount of out-of-domain data is added to the training data, the quality of translation can drop. On the other hand, training an SMT system on a small amount of training material for given indomain data leads to narrow lexical coverage which again results in a low translation quality. In this paper, (i) we explore domain-adaptation techniques to combine large out-of-domain training data with small-scale in-domain training data for EnglishâHindi statistical machine translation and (ii) we cluster large out-of-domain training data to extract sentences similar to in-domain sentences and apply adaptation techniques to combine clustered sub-corpora
with in-domain training data into a unified framework, achieving a 0.44 absolute corresponding to a 4.03% relative improvement in terms of BLEU over the baseline
Lessons learned in multilingual grounded language learning
Recent work has shown how to learn better visual-semantic embeddings by
leveraging image descriptions in more than one language. Here, we investigate
in detail which conditions affect the performance of this type of grounded
language learning model. We show that multilingual training improves over
bilingual training, and that low-resource languages benefit from training with
higher-resource languages. We demonstrate that a multilingual model can be
trained equally well on either translations or comparable sentence pairs, and
that annotating the same set of images in multiple language enables further
improvements via an additional caption-caption ranking objective.Comment: CoNLL 201
Findings of the 2019 Conference on Machine Translation (WMT19)
This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019.
Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation
Stronger Baselines for Trustable Results in Neural Machine Translation
Interest in neural machine translation has grown rapidly as its effectiveness
has been demonstrated across language and data scenarios. New research
regularly introduces architectural and algorithmic improvements that lead to
significant gains over "vanilla" NMT implementations. However, these new
techniques are rarely evaluated in the context of previously published
techniques, specifically those that are widely used in state-of-theart
production and shared-task systems. As a result, it is often difficult to
determine whether improvements from research will carry over to systems
deployed for real-world use. In this work, we recommend three specific methods
that are relatively easy to implement and result in much stronger experimental
systems. Beyond reporting significantly higher BLEU scores, we conduct an
in-depth analysis of where improvements originate and what inherent weaknesses
of basic NMT models are being addressed. We then compare the relative gains
afforded by several other techniques proposed in the literature when starting
with vanilla systems versus our stronger baselines, showing that experimental
conclusions may change depending on the baseline chosen. This indicates that
choosing a strong baseline is crucial for reporting reliable experimental
results.Comment: To appear at the Workshop on Neural Machine Translation (WNMT
Investigating cross-language speech retrieval for a spontaneous conversational speech collection
Cross-language retrieval of spontaneous speech combines the challenges of working with noisy automated transcription and language translation. The CLEF 2005 Cross-Language Speech Retrieval (CL-SR) task provides a standard test collection to investigate these challenges. We show that we can improve retrieval performance: by careful selection of the term weighting scheme; by decomposing automated transcripts into
phonetic substrings to help ameliorate transcription
errors; and by combining automatic transcriptions with manually-assigned metadata. We further show that topic translation with online machine translation resources
yields effective CL-SR
An incremental three-pass system combination framework by combining multiple hypothesis alignment methods
System combination has been applied successfully to various machine translation tasks in recent years. As is known, the hypothesis alignment method is a critical factor for the
translation quality of system combination. To date, many effective hypothesis alignment metrics have been proposed and applied to the system combination, such as TER, HMM,
ITER, IHMM, and SSCI. In addition, Minimum Bayes-risk (MBR) decoding and confusion networks (CN) have become state-of-the-art techniques in system combination. In this paper,
we examine different hypothesis alignment approaches and investigate how much the hypothesis alignment results impact on system combination, and finally present a three-pass system combination strategy that can combine hypothesis alignment results derived from multiple alignment metrics to generate a better translation. Firstly, these different alignment metrics are carried out to align the backbone and hypotheses, and the individual CNs are built corresponding to each set of alignment results; then we construct a âsuper networkâ by merging the multiple metric-based CNs to generate a consensus output. Finally a modified MBR network approach is employed to find the best overall translation. Our proposed strategy outperforms the best single confusion network as well as the best single system in our experiments on the NIST Chinese-to-English test set and the WMT2009 English-to-French system combination shared test set
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