| 1 |
Generación de textos en ruso mediante técnicas de Aprendizaje Automático para la industria del lenguaje
|
|
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Some Contributions to Interactive Machine Translation and to the Applications of Machine Translation for Historical Documents
|
|
|
|
BASE
|
|
Show details
|
|
|
| 3 |
A study on the impact of neural architectures for Unsupervised Machine Translation
|
|
|
|
BASE
|
|
Show details
|
|
|
| 4 |
On The Use of Monolingual Corpus for Training Neural Machine Translation Systems ; Uso de corpus monolíngües para el entrenamiento de traductores neuronales
|
|
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Limitations and challenges of unsupervised cross-lingual pre-training
|
|
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Interactive translation with neural models based on the use of mouse actions and confidence measures ; Traducción Interactiva con modelos neuronales basada en el uso del ratón y medidas de confianza
|
|
|
|
BASE
|
|
Show details
|
|
|
| 9 |
Traducción automática de conjuntos de datos para la construcción de sistemas de pregunta/respuesta mediante aprendizaje automático.
|
|
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Filtering of Noisy Parallel Corpora Based on Hypothesis Generation
|
|
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Advanced techniques for domain adaptation in Statistical Machine Translation
|
|
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Demonstration of a Neural Machine Translation System with Online Learning for Translators
|
|
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Vector sentences representation for data selection in statistical machine translation
|
|
|
|
BASE
|
|
Show details
|
|
|
| 14 |
A Neural, Interactive-predictive System for Multimodal Sequence to Sequence Tasks
|
|
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Discriminative ridge regression algorithm for adaptation in statistical machine translation
|
|
|
|
Abstract:
[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-Ríos, 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/s10044-018-0720-5 ; S ; 1293 ; 1305 ; 22 ; 4 ; Barrachina 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):3–28 ; Bojar 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 Linguistics ; Brown PF, Pietra VJD, Pietra SAD, Mercer RL (1993) The mathematics of statistical machine translation: parameter estimation. Comput Ling 19:263–311 ; Callison-Burch C, Koehn P, Monz C, Peterson K, Przybocki M, Zaidan OF (2010) Findings of the 2010 joint workshop on statistical machine translation and metrics for machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 17–53 ; Chen B, Cherry C (2014) A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the workshop on statistical machine translation, pp 362–367 ; Cherry C, Foster G (2012) Batch tuning strategies for statistical machine translation. In: Proceedings of the North American chapter of the association for computational linguistics, pp 427–436 ; Clark JH, Dyer C, Lavie A, Smith NA (2011) Better hypothesis testing for statistical machine translation: controlling for optimizer instability. In: Proceedings of the annual meeting of the association for computational linguistics, pp 176–181 ; Crammer K, Dekel O, Keshet J, Shalev-Shwartz S, Singer Y (2006) Online passive-aggressive algorithms. J Mach Learn Res 7:551–585 ; Hasler E, Haddow B, Koehn P (2011) Margin infused relaxed algorithm for moses. Prague Bull Math Ling 96:69–78 ; Hopkins M, May J (2011) Tuning as ranking. In: Proceedings of the conference on empirical methods in natural language processing, pp 1352–1362 ; Kneser R, Ney H (1995) Improved backing-off for m-gram language modeling. In: Proceedings of the international conference on acoustics, speech and signal processing, pp 181–184 ; Koehn P (2005) Europarl: a parallel corpus for statistical machine translation. In: Proceedings of the machine translation summit, pp 79–86 ; Koehn P (2010) Statistical machine translation. Cambridge University Press, Cambridge ; Koehn 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 177–180 ; Lavie 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 376–387 ; Marie 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 554–559 ; Martínez-Gómez P, Sanchis-Trilles G, Casacuberta F (2012) Online adaptation strategies for statistical machine translation in post-editing scenarios. Pattern Recogn 45(9):3193–3203 ; Nakov P, Vogel S (2017) Robust tuning datasets for statistical machine translation. arXiv:1710.00346 ; Neubig G, Watanabe T (2016) Optimization for statistical machine translation: a survey. Comput Ling 42(1):1–54 ; Och FJ (2003) Minimum error rate training in statistical machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 160–167 ; Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Comput Ling 29:19–51 ; Papineni K, Roukos S, Ward T, Zhu WJ (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the international conference on acoustics, speech and signal processing, pp 311–318 ; Sanchis-Trilles G, Casacuberta F (2010) Log-linear weight optimisation via Bayesian adaptation in statistical machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 1077–1085 ; Sanchis-Trilles G, Casacuberta F (2015) Improving translation quality stability using Bayesian predictive adaptation. Comput Speech Lang 34(1):1–17 ; Snover M, Dorr B, Schwartz R, Micciulla L, Makhoul J (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the annual meeting of the association for machine translation in the Americas, pp 223–231 ; Sokolov A, Yvon F (2011) Minimum error rate training semiring. In: Proceedings of the annual conference of the European association for machine translation, pp 241–248 ; Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. Pattern Anal Mach Intell 22(8):747–757 ; Stolcke A (2002) Srilm—an extensible language modeling toolkit. In: Proceedings of the international conference on spoken language processing, pp 901–904 ; Tiedemann J (2009) News from opus—a collection of multilingual parallel corpora with tools and interfaces. In: Proceedings of the recent advances in natural language processing, pp 237–248 ; Tiedemann J (2012) Parallel data, tools and interfaces in opus. In: Proceedings of the language resources and evaluation conference, pp 2214–2218
|
|
Keyword:
Adaptation; Discriminative ridge regression algorithm; LENGUAJES Y SISTEMAS INFORMATICOS; Log-linear model; Log-linear weights; Statistical machine translation
|
|
URL: https://doi.org/10.1007/s10044-018-0720-5
http://hdl.handle.net/10251/157503
|
|
BASE
|
|
Hide details
|
|
|
| 16 |
NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning
|
|
|
|
BASE
|
|
Show details
|
|
|
| 20 |
Traducción Automática Interactiva Basada en Segmentos de Palabras
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
