Ashish Venugopal

Effective Phrase Translation Extraction from Alignment Models

Phrase level translation models are effective in improving translation quality by addressing the problem of local re-ordering across language boundaries. Methods that attempt to fundamentally modify the traditional IBM translation model to incorporate phrases typically do so at a prohibitive computational cost. We present a technique that begins with improved IBM models to create phrase level knowledge sources that effectively represent local as well as global phrasal context. Our method is robust to noisy alignments at both the sentence and corpus level, delivering high quality phrase level translation pairs that contribute to significant improvements in translation quality (as measured by the BLEU metric) over word based lexica as well as a competing alignment based method.

Bridging the Inflection Morphology Gap for Arabic Statistical Machine Translation

Statistical machine translation (SMT) is based on the ability to effectively learn word and phrase relationships from parallel corpora, a process which is considerably more difficult when the extent of morphological expression differs significantly across the source and target languages. We present techniques that select appropriate word segmentations in the morphologically rich source language based on contextual relationships in the target language. Our results take advantage of existing word level morphological analysis components to improve translation quality above state-of-the-art on a limited-data Arabic to English speech translation task.

Grammar based statistical MT on Hadoop An end-to-end toolkit for large scale PSCFG based MT

Grammar based statistical MT on Hadoop: An end-to-end toolkit for large scale PSCFG based MT This paper describes the open-source Syntax Augmented Machine Translation (SAMT) 1on Hadoop toolkit—an end-to-end grammar based machine statistical machine translation framework running on the Hadoop implementation of the MapReduce programming model. We present the underlying methodology of the SAMT approach with detailed instructions that describe how to use the toolkit to build grammar based systems for large scale translation tasks.

Training and Evaluating Error Minimization Decision Rules for Statistical Machine Translation

Decision rules that explicitly account for non-probabilistic evaluation metrics in machine translation typically require special training, often to estimate parameters in exponential models that govern the search space and the selection of candidate translations. While the traditional Maximum A Posteriori (MAP) decision rule can be optimized as a piecewise linear function in a greedy search of the parameter space, the Minimum Bayes Risk (MBR) decision rule is not well suited to this technique, a condition that makes past results difficult to compare. We present a novel training approach for non-tractable decision rules, allowing us to compare and evaluate these and other decision rules on a large scale translation task, taking advantage of the high dimensional parameter space available to the phrase based Pharaoh decoder. This comparison is timely, and important, as decoders evolve to represent more complex search space decisions and are evaluated against innovative evaluation metrics of translation quality.

FrontDesk: an enterprise class Web-based software system for programming assignment submission, feedback dissemination, and grading automation

The problem of managing an effective relationship between course staff and students in large programming courses admits no trivial solution. Students often complain of lack of feedback, slow assignment grading times, and a gap in communication between them and the course staff responsible for evaluating their work. In addition, course staff feels powerless to help because of the complexity and sheer numbers of students involved in such courses. In this paper, we describe our Web-based distributed application, FrontDesk that attempts to bridge the feedback and communication gap that these courses suffer from. FrontDesk provides tools for students to submit their work through the Web and to receive rich, informative feedback. It provides course staff with the ability to give effective subjective feedback for large courses and to automate objective programming assignment correctness testing in a flexible, distributed, and efficient manner.