19,855 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
Discriminative ridge regression algorithm for adaptation in statistical machine translation
[EN] We present a simple and reliable method for estimating the log-linear weights of a state-of-the-art machine translation system, which takes advantage of the method known as discriminative ridge regression (DRR). Since inappropriate weight estimations lead to a wide variability of translation quality results, reaching a reliable estimate for such weights is critical for machine translation research. For this reason, a variety of methods have been proposed to reach reasonable estimates. In this paper, we present an algorithmic description and empirical results proving that DRR is able to provide comparable translation quality when compared to state-of-the-art estimation methods [i.e. MERT and MIRA], with a reduction in computational cost. Moreover, the empirical results reported are coherent across different corpora and language pairs.The research leading to these results were partially supported by projects CoMUN-HaT-TIN2015-70924-C2-1-R (MINECO/FEDER) and PROMETEO/2018/004. We also acknowledge NVIDIA for the donation of a GPU used in this work.Chinea-RĂos, M.; Sanchis-Trilles, G.; Casacuberta Nolla, F. (2019). Discriminative ridge regression algorithm for adaptation in statistical machine translation. Pattern Analysis and Applications. 22(4):1293-1305. https://doi.org/10.1007/s10044-018-0720-5S12931305224Barrachina S, Bender O, Casacuberta F, Civera J, Cubel E, Khadivi S, Lagarda A, Ney H, Tomás J, Vidal E et al (2009) Statistical approaches to computer-assisted translation. Comput Ling 35(1):3–28Bojar O, Buck C, Federmann C, Haddow B, Koehn P, Monz C, Post M, Specia L (eds) (2014) Proceedings of the ninth workshop on statistical machine translation. Association for Computational LinguisticsBrown PF, Pietra VJD, Pietra SAD, Mercer RL (1993) The mathematics of statistical machine translation: parameter estimation. 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Cambridge University Press, CambridgeKoehn P, Hoang H, Birch A, Callison-Burch C, Federico M, Bertoldi N, Cowan B, Shen W, Moran C, Zens R, Dyer C, Bojar O, Constantin A, Herbst E (2007) Moses: open source toolkit for statistical machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 177–180Lavie MDA (2014) Meteor universal: language specific translation evaluation for any target language. In: Proceedings of the annual meeting of the association for computational linguistics, pp 376–387Marie B, Max A (2015) Multi-pass decoding with complex feature guidance for statistical machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 554–559MartĂnez-GĂłmez P, Sanchis-Trilles G, Casacuberta F (2012) Online adaptation strategies for statistical machine translation in post-editing scenarios. 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Competence-based Curriculum Learning for Neural Machine Translation
Current state-of-the-art NMT systems use large neural networks that are not
only slow to train, but also often require many heuristics and optimization
tricks, such as specialized learning rate schedules and large batch sizes. This
is undesirable as it requires extensive hyperparameter tuning. In this paper,
we propose a curriculum learning framework for NMT that reduces training time,
reduces the need for specialized heuristics or large batch sizes, and results
in overall better performance. Our framework consists of a principled way of
deciding which training samples are shown to the model at different times
during training, based on the estimated difficulty of a sample and the current
competence of the model. Filtering training samples in this manner prevents the
model from getting stuck in bad local optima, making it converge faster and
reach a better solution than the common approach of uniformly sampling training
examples. Furthermore, the proposed method can be easily applied to existing
NMT models by simply modifying their input data pipelines. We show that our
framework can help improve the training time and the performance of both
recurrent neural network models and Transformers, achieving up to a 70%
decrease in training time, while at the same time obtaining accuracy
improvements of up to 2.2 BLEU
Discourse Structure in Machine Translation Evaluation
In this article, we explore the potential of using sentence-level discourse
structure for machine translation evaluation. We first design discourse-aware
similarity measures, which use all-subtree kernels to compare discourse parse
trees in accordance with the Rhetorical Structure Theory (RST). Then, we show
that a simple linear combination with these measures can help improve various
existing machine translation evaluation metrics regarding correlation with
human judgments both at the segment- and at the system-level. This suggests
that discourse information is complementary to the information used by many of
the existing evaluation metrics, and thus it could be taken into account when
developing richer evaluation metrics, such as the WMT-14 winning combined
metric DiscoTKparty. We also provide a detailed analysis of the relevance of
various discourse elements and relations from the RST parse trees for machine
translation evaluation. In particular we show that: (i) all aspects of the RST
tree are relevant, (ii) nuclearity is more useful than relation type, and (iii)
the similarity of the translation RST tree to the reference tree is positively
correlated with translation quality.Comment: machine translation, machine translation evaluation, discourse
analysis. Computational Linguistics, 201
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