Bidirectional Decoding for Statistical Machine Translation

This paper describes the right-to-left decoding method, which translates an input string by generating in right-to-left direction. In addition, presented is the bidirectional decoding method, that can take both of the advantages of left-to-right and right-to-left decoding method by generating output in both ways and by merging hypothesized partial outputs of two directions. The experimental results on Japanese and English translation showed that the right-to-left was better for Englith-to-Japanese translation, while the left-to-right was suitable for Japanese-to-English translation. It was also observed that the bidirectional method was better for English-to-Japanese translation

Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation

Traditional Neural machine translation (NMT) involves a fixed training procedure where each sentence is sampled once during each epoch. In reality, some sentences are well-learned during the initial few epochs; however, using this approach, the well-learned sentences would continue to be trained along with those sentences that were not well learned for 10-30 epochs, which results in a wastage of time. Here, we propose an efficient method to dynamically sample the sentences in order to accelerate the NMT training. In this approach, a weight is assigned to each sentence based on the measured difference between the training costs of two iterations. Further, in each epoch, a certain percentage of sentences are dynamically sampled according to their weights. Empirical results based on the NIST Chinese-to-English and the WMT English-to-German tasks depict that the proposed method can significantly accelerate the NMT training and improve the NMT performance.Comment: Revised version of ACL-201

ScrumSourcing: Challenges of Collaborative Post-editing for Rugby World Cup 2019

This paper describes challenges facing the ScrumSourcing project to create a neural machine translation (NMT) service aiding interaction between Japanese- and English-speaking fans during Rugby World Cup 2019 in Japan. This is an example of «domain adaptation». The best training data for adapting NMT is large volumes of translated sentences typical of the domain. In reality, however, such parallel data for rugby does not exist. The problem is compounded by a marked asymmetry between the two languages in conventions for post-match reports; and the almost total absence of in-match commentaries in Japanese. In post-editing the NMT output to incrementally improve quality via retraining, volunteer rugby fans will play a crucial role in determining a new genre in Japanese. To avoid de-motivating the volunteers at the outset we undertake an initial adaptation of the system using terminological data. This paper describes the compilation of this data and its effects on the quality of the systems’ output.Este documento describe los retos a los que se enfrenta el proyecto ScrumSourcing para crear un servicio de traducción automática neuronal (NMT) que ayude a la interacción entre los aficionados de habla japonesa e inglesa durante la Copa Mundial de Rugby de 2019 en Japón. Este es un ejemplo de «adaptación al dominio». Los mejores datos de entrenamiento para adaptar la NMT son grandes volúmenes de oraciones traducidas típicas del dominio. Sin embargo, en la realidad no existen tales datos paralelos para el rugby. El problema se agrava por una marcada asimetría entre las dos lenguas en las convenciones para los informes posteriores al partido y la ausencia casi total de comentarios emitidos en directo durante el partido en japonés. En la post-edición de la producción de la NMT para mejorar de forma incremental la calidad a través del reentrenamiento, los voluntarios aficionados al rugby desempeñarán un papel crucial en la determinación de un nuevo género en japonés. Para evitar desmotivar a los voluntarios desde el principio, emprenderemos una adaptación inicial del sistema utilizando datos terminológicos. Este documento describe la compilación de estos datos y sus efectos en la calidad de la producción de los sistemas

Improving fast align by reordering,”

Abstract fast align is a simple, fast, and efficient approach for word alignment based on the IBM model 2. fast align performs well for language pairs with relatively similar word orders; however, it does not perform well for language pairs with drastically different word orders. We propose a segmenting-reversing reordering process to solve this problem by alternately applying fast align and reordering source sentences during training. Experimental results with JapaneseEnglish translation demonstrate that the proposed approach improves the performance of fast align significantly without the loss of efficiency. Experiments using other languages are also reported