Online adaptation strategies for statistical machine translation in post-editing scenarios

Abstract

One of the most promising approaches to machine translation consists in formulating the problem by means of a pattern recognition approach. By doing so, there are some tasks in which online adaptation is needed in order to adapt the system to changing scenarios. In the present work, we perform an exhaustive comparison of four online learning algorithms when combined with two adaptation strategies for the task of online adaptation in statistical machine translation. Two of these algorithms are already well-known in the pattern recognition community, such as the perceptron and passive-aggressive algorithms, but here they are thoroughly analyzed for their applicability in the statistical machine translation task. In addition, we also compare them with two novel methods, i.e., Bayesian predictive adaptation and discriminative ridge regression. In statistical machine translation, the most successful approach is based on a log-linear approximation to a posteriori distribution. According to experimental results, adapting the scaling factors of this log-linear combination of models using discriminative ridge regression or Preprint submitted to Pattern Recognition November 22, 2011 Bayesian predictive adaptation yields the best performance.

Human Effort and Machine Learnability in Computer Aided Translation

“…Incremental MT learning has been investigated several times, usually starting from no data (Barrachina et al, 2009;Ortiz-Martínez et al, 2010), via simulated post-editing (Martínez-Gómez et al, 2012;Denkowski et al, 2014a), or via re-ranking (Wäschle et al, 2013). No previous experiments combined large-scale baselines, full re-tuning of the model weights, and HTER optimization.…”

Leveraging Online User Feedback to Improve Statistical Machine Translation

“…To overcome this problem, several incremental alignment models have been proposed in the literature (Levenberg, Callison-Burch, & Osborne, 2010). With the exception of the stream-based translation approach, which adds or updates the original TM scores according to the new material (Ortiz-Martínez, García-Varea, & Casacuberta, 2010;Martínez-Gómez, Sanchis-Trilles, & Casacuberta, 2012;Mathur et al, 2013), the adaptation step is usually carried out by creating specific translation tables from the edited translations (using the standard phrase-extraction and phrase-scoring algorithms) and then combining them with the original translation tables. It is important to note that most of the work on incremental adaptation has been tested in the scenarios where references are used instead of UEs.…”

“…However, only simulated data, using references instead of actual human UEs, were considered in this work. Ortiz-Martínez et al (2010) and Martínez-Gómez et al (2012) applied an incremental version of the Expectation Maximization (EM) algorithm (Neal & Hinton, 1998) that minimizes an error function with small sequences of mini-batched data. This paradigm is commonly known as stream-based translation, as small portions of data are processed over time.…”

Adaptive Quality Estimation for Machine Translation

“…On the MT system side, research on adaptive approaches tailored to interactive SMT and CAT scenarios explored the online learning protocol (Littlestone, 1988) to improve various aspects of the decoding process (Cesa-Bianchi et al, 2008;Ortiz-Martínez et al, 2010;Martínez-Gómez et al, 2011;Martínez-Gómez et al, 2012;Mathur et al, 2013;Bertoldi et al, 2013).…”