This paper describes the IIT Bombay’s submission as a part of the shared task in WAT 2016 for English–Indonesian language pair. The results reported here are for both the direction of the language pair. Among the various approaches experimented, Operation Sequence Model (OSM) and Neural Language Model have been submitted for WAT. The OSM approach integrates translation and reordering process re...

This paper presents the comparison of how using different neural network based language modelling tools for selecting the best candidate fragments affects the final output translation quality in a hybrid multi-system machine translation setup. Experiments were conducted by comparing perplexity and BLEU scores on common test cases using the same training data set. A 12-gram statistical language ...

In Minimum Error Rate Training (MERT), the parameters of an SMT system are tuned on a certain evaluation metric to improve translation quality. In this paper, we present empirical results in which parameters tuned on one metric (e.g. BLEU) may not lead to optimal scores on the same metric. The score can be improved significantly by tuning on an entirely different metric (e.g. METEOR, by 0.82 BL...

We present a Minimum Bayes Risk (MBR) decoder for statistical machine translation. The approach aims to minimize the expected loss of translation errors with regard to the BLEU score. We show that MBR decoding on N -best lists leads to an improvement of translation quality. We report the performance of the MBR decoder on four different tasks: the TCSTAR EPPS Spanish-English task 2006, the NIST ...

In natural language processing tasks performance of the models is often measured with some non-differentiable metric, such as BLEU score. To use efficient gradientbased methods for optimization, it is a common workaround to optimize some surrogate loss function. This approach is effective if optimization of such loss also results in improving target metric. The corresponding problem is referred...

The encoder-decoder framework for neural machine translation (NMT) has been shown effective in large data scenarios, but is much less effective for low-resource languages. We present a transfer learning method that significantly improves BLEU scores across a range of low-resource languages. Our key idea is to first train a high-resource language pair (the parent model), then transfer some of th...

We propose a novel, language-independent approach for improving machine translation from a resource-poor language to X by adapting a large bi-text for a related resource-rich language and X (the same target language). We assume a small bi-text for the resourcepoor language to X pair, which we use to learn word-level and phrase-level paraphrases and cross-lingual morphological variants between t...

This paper describes a sentence selection strategy for tuning a statistical machine translation system based on Moses that translates Spanish into English. This work proposes two techniques that allow selecting the more similar source sentences of the development corpus to the sentences to translate (source test sentences). With this selection, better model weights are obtained to be used later...

The paper presents series of experiments that aim to find best method how to treat multi-word expressions (MWE) in machine translation task. Methods have been investigated in a framework of statistical machine translation (SMT) for translation form English into Latvian. MWE candidates have been extracted using pattern-based and statistical approaches. Different techniques for MWE integration in...

The present work reports the development of Manipuri-English bidirectional statistical machine translation systems. In the English-Manipuri statistical machine translation system, the role of the suffixes and dependency relations on the source side and case markers on the target side are identified as important translation factors. A parallel corpus of 10350 sentences from news domain is used f...