by first doing the types of detailed language analysis employed in Sampark. “What one can do in the future is to first do monolingual analysis of one or both sides in paralleled corpora, and then use that to improve the quality of machine learning from the parallel corpora,” he says. “So what we have done would also be useful if larger parallel corpora became available tomorrow.”

Another advantage of the transfer approach, says AT&T’s Bangalore, is its generalizability. “If you give me a parallel corpus dealing with financial news, and I train it up with millions of sentences of that sort, and two days later you say, ‘Translate a sports article,’ it’s not going to perform as well.”

But that kind of application domain change has been explicitly anticipated by Sampark’s developers. The first version, rolling out now language-by-language, is general purpose and optimized for tourism-related uses, but it will be made available to large users who wish to customize it for other domains, says Dipti Sharma, an associate professor at IIIT-H. That would involve building a new domain dictionary, incorporating rules that handle domain-specific grammatical structures, and perhaps retraining some modules such as Part of Speech Tagger and Named Entity Recognizer.

The effort required to make those changes is minimized by building on the existing multilingual dictionary,

Sharma says. It is sense- or meaning-based, so that for one domain or language, “bank,” for example, would most likely represent a financial institution, but for another it might refer to the edge of a river, Sharma says. The dictionary currently allows translation among nine languages.

Sangal says the language-translation system has two especially noteworthy attributes. First, the linguistic analysis based on Panini is “extremely good,” he says. “It was initially chosen for Indian languages, but we find it is also suitable for other languages.” Initially, hard work is needed, he says, in setting it up by developing standards for parts-of-speech tags and dependency tree labels and for figuring out ways to handle unique language constructs.

The second attribute of special note is the system’s software architecture. It is an open architecture in which all modules produce output in Shakti Standard Format (SSF). The architecture allows modules written in different programming languages to be plugged in. Readability of SSF helps in development and debugging because the input and output of any module can be easily seen. Also, a dashboard tool supports the architecture in a variety of ways. Custom written, it is “ extremely robust,” Sangal says. “If a module fails to perform a proper analysis, the next module will still work, albeit in a degraded mode. So the system nev-

er gives up; it always tries to produce something.”

 

Further Reading

Naskar, S. and Bandyopadhyay, S. Use of machine translation in India: current status. Machine Translation Review 15, Dec. 2005. Bharati, A., Sangal, R., Mishra, D., V., Sriram, T., Papi Reddy handling multi-word expressions explicit linguistic rules in an MT system. Proceedings of the Seventh International Conference on Text, Speech and Dialogue, 2004.

Lavie, A., Vogel, S., Levin, L., Peterson, E., Probst, K., Llitjos, A.F., Reynolds, R., Carbonell, J., Cohen, R. Experiments with a hindi-to-English transfer-based MT system under a miserly data scenario. ACM Trans. on Asian Language Processing 2, 2, June 2003.

Bharati, A., Chaitanya, V., Kulkarni, A., Sangal, R., Umamaheshwara Rao, G. Anusaaraka: overcoming the language barrier in India. Anuvad: Approaches to Translation, Sage, New Delhi, 2001.

Manning, C.

Foundations of Statistical Natural Language Processing. MIT Press, Cambridge, MA, 1999.

Bharati, A. and Sangal, R.. Parsing free-order languages in the Paninian framework. Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, June, 1993.

Gary Anthes is a technology writer and editor based in arlington, Va.

© 2010 aCM 0001-0782/10/0100 $10.00

Milestones
SIGUCCS Hall of Fame and Other CS Awards

select members of the computer science community were recently honored for their innovative service and research.

siGuccs HAll of fAme Recognized for their years of service, the 2009 inductees to the siGUCCs Hall of Fame are nancy Bauer-Runyan, Ross University; Jim Kerlin; Lynnell Lacy, University of illinois at Urbana-Champaign; teresa (terry) Lockard, University of Virginia; and Glenn Ricart.

GoRDon Bell PRizes the Gordon Bell Award was presented at sC09 to recognize

outstanding achievement in high-performance computing applications. the purpose of the award is to track the progress over time of parallel computing, with particular emphasis on rewarding innovation in applying high-performance computing to applications in science.

A team led by tsuyoshi Hamada of nagasaki University won for its paper “ 42 tFlops Hierarchical n-body simulations on GpUs with Applications in both Astrophysics and turbulence” in the lower price/performance category.

there were two winners in the special category. A team led by David e. shaw of D.e. shaw Research won for its paper “Millisecond-scale Molecular Dynamics simulations on Anton” and a team led by Rajagopal Ananthanarayanan of iBM Almaden Research Center won for its paper “the Cat is out of the Bag: Cortical simulations with 109 neurons, 1013 synapses.”

in the peak performance category, a team led by Markus eisenbach of oak Ridge national Laboratory won for its paper “A scalable Method for Ab initio Computation of Free energies in nanoscale systems.”

Penn Y cRAne AWARD Robert paterson, vice president for information technology, planning and research at Molloy College, received the penny Crane Award at siGUCCs in recognition of significant contributions to siGUCCs and computing in higher education.

DiAnA AWARD on behalf of Apple, sandy Korzenny, director of Apple product documentation, received the Diana Award, which siGDoC presents every two years to an organization, institution, or business for its long-term contribution to the field of communication design.