Shyam Upadhyay

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IllinoisSL is a Java library for learning structured prediction models. It supports struc-tured Support Vector Machines and structured Perceptron. The library consists of a core learning module and several applications, which can be executed from command-lines. Documentation is provided to guide users. In Comparison to other structured learning libraries,(More)
Training a structured prediction model involves performing several loss-augmented inference steps. Over the lifetime of the training, many of these inference problems, although different, share the same solution. We propose AI-DCD, an Amortized Inference framework for Dual Coordinate Descent method, an approximate learning algorithm, that accelerates the(More)
Despite interest in using cross-lingual knowledge to learn word embeddings for various tasks, a systematic comparison of the possible approaches is lacking in the literature. We perform an extensive evaluation of four popular approaches of inducing cross-lingual embeddings, each requiring a different form of supervision, on four typologically different(More)
Training structured prediction models is time-consuming. However, most existing approaches only use a single machine, thus, the advantage of computing power and the capacity for larger data sets of multiple machines have not been exploited. In this work, we propose an efficient algorithm for distributedly training structured support vector machines based on(More)
In this year's WMT translation task, Finnish-English was introduced as a language pair of competition for the first time. We present experiments examining several variations on a morphologically-aware statistical phrase-based machine translation system for translating Finnish into English. Our system variations attempt to mitigate the issue of rich(More)
We propose a new evaluation for automatic solvers for algebra word problems, which can identify reasoning mistakes that existing evaluations overlook. Our proposal is to use derivations for evaluations, which reflect the reasoning process of the solver by explaining how the equation system was constructed. We accomplish this by developing an algorithm for(More)
Identifying mathematical relations expressed in text is essential to understanding a broad range of natural language text from election reports, to financial news, to sport commentaries to mathematical word problems. This paper focuses on identifying and understanding mathematical relations described within a single sentence. We introduce the problem of(More)
Most events described in a news article are background events – only a small number are noteworthy, and a even smaller number serve as the trigger for writing of that article. Although these events are difficult to identify, they are crucial to NLP tasks such as first story detection, document summarization and event coreference, and to many applications of(More)
Dataless text classification [Chang et al., 2008] is a classification paradigm which maps documents into a given label space without requiring any annotated training data. This paper explores a cross-lingual variant of this paradigm, where documents in multiple languages are classified into an English label space. We use CLESA (cross-lingual explicit(More)
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