We propose minimum risk training for end-to-end neural machine translation. Unlike conventional maximum likelihood estimation, minimum risk training is capable of optimizing model parameters directly with respect to arbitrary evaluation metrics, which are not necessarily differentiable. Experiments show that our approach achieves significant improvements over maximum likelihood estimation on a state-of-the-art neural machine translation system across various languages pairs. Transparent to architectures, our approach can be applied to more neural networks and potentially benefit more NLP tasks.
Our work originated from the minimum risk training algorithms in conventional statistical machine translation @cite_24 @cite_2 @cite_4 . Och describes a smoothed error count to allow calculating gradients, which directly inspires us to use a parameter @math to adjust the smoothness of the objective function. As neural networks are non-linear, our approach has to minimize the expected loss on the sentence level rather than the loss of 1-best translations on the corpus level. Smith and Eisner introduce minimum risk annealing for training log-linear models that is capable of gradually annealing to focus on the 1-best hypothesis. apply minimum risk training to learning phrase translation probabilities. leverage MRT for learning continuous phrase representations for statistical machine translation. The difference is that they use MRT to optimize a sub-model of SMT while we are interested in directly optimizing end-to-end neural translation models.
@cite_24 @cite_4 @cite_2
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When training the parameters for a natural language system, one would prefer to minimize 1-best loss (error) on an evaluation set. Since the error surface for many natural language problems is piecewise constant and riddled with local minima, many systems instead optimize log-likelihood, which is conveniently differentiable and convex. We propose training instead to minimize the expected loss, or risk. We define this expectation using a probability distribution over hypotheses that we gradually sharpen (anneal) to focus on the 1-best hypothesis. Besides the linear loss functions used in previous work, we also describe techniques for optimizing nonlinear functions such as precision or the BLEU metric. We present experiments training log-linear combinations of models for dependency parsing and for machine translation. In machine translation, annealed minimum risk training achieves significant improvements in BLEU over standard minimum error training. We also show improvements in labeled dependency parsing. We propose minimum risk training for end-to-end neural machine translation. Unlike conventional maximum likelihood estimation, minimum risk training is capable of optimizing model parameters directly with respect to arbitrary evaluation metrics, which are not necessarily differentiable. Experiments show that our approach achieves significant improvements over maximum likelihood estimation on a state-of-the-art neural machine translation system across various languages pairs. Transparent to architectures, our approach can be applied to more neural networks and potentially benefit more NLP tasks. Current statistical machine translation (SMT) systems are trained on sentence-aligned and word-aligned parallel text collected from various sources. Translation model parameters are estimated from the word alignments, and the quality of the translations on a given test set depends on the parameter estimates. There are at least two factors affecting the parameter estimation: domain match and training data quality. This paper describes a novel approach for automatically detecting and down-weighing certain parts of the training corpus by assigning a weight to each sentence in the training bitext so as to optimize a discriminative objective function on a designated tuning set. This way, the proposed method can limit the negative effects of low quality training data, and can adapt the translation model to the domain of interest. It is shown that such discriminative corpus weights can provide significant improvements in Arabic-English translation on various conditions, using a state-of-the-art SMT system. We compare two pivot strategies for phrase-based statistical machine translation (SMT), namely phrase translation and sentence translation. The phrase translation strategy means that we directly construct a phrase translation table (phrase-table) of the source and target language pair from two phrase-tables; one constructed from the source language and English and one constructed from English and the target language. We then use that phrase-table in a phrase-based SMT system. The sentence translation strategy means that we first translate a source language sentence into n English sentences and then translate these n sentences into target language sentences separately. Then, we select the highest scoring sentence from these target sentences. We conducted controlled experiments using the Europarl corpus to evaluate the performance of these pivot strategies as compared to directly trained SMT systems. The phrase translation strategy significantly outperformed the sentence translation strategy. Its relative performance was 0.92 to 0.97 compared to directly trained SMT systems.