Yakov Kronrod


2017

pdf bib
Deep Active Learning for Named Entity Recognition
Yanyao Shen | Hyokun Yun | Zachary Lipton | Yakov Kronrod | Animashree Anandkumar
Proceedings of the 2nd Workshop on Representation Learning for NLP

Deep neural networks have advanced the state of the art in named entity recognition. However, under typical training procedures, advantages over classical methods emerge only with large datasets. As a result, deep learning is employed only when large public datasets or a large budget for manually labeling data is available. In this work, we show otherwise: by combining deep learning with active learning, we can outperform classical methods even with a significantly smaller amount of training data.

2011

pdf bib
The Value of Monolingual Crowdsourcing in a Real-World Translation Scenario: Simulation using Haitian Creole Emergency SMS Messages
Chang Hu | Philip Resnik | Yakov Kronrod | Vladimir Eidelman | Olivia Buzek | Benjamin B. Bederson
Proceedings of the Sixth Workshop on Statistical Machine Translation

2010

pdf bib
Improving Translation via Targeted Paraphrasing
Philip Resnik | Olivia Buzek | Chang Hu | Yakov Kronrod | Alex Quinn | Benjamin B. Bederson
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

pdf bib
Position Paper: Improving Translation via Targeted Paraphrasing
Yakov Kronrod | Philip Resnik | Olivia Buzek | Chang Hu | Alex Quinn | Ben Bederson
Proceedings of the Workshop on Collaborative Translation: technology, crowdsourcing, and the translator perspective

Targeted paraphrasing is a new approach to the problem of obtaining cost-effective, reasonable quality translation that makes use of simple and inexpensive human computations by monolingual speakers in combination with machine translation. The key insight behind the process is that it is possible to spot likely translation errors with only monolingual knowledge of the target language, and it is possible to generate alternative ways to say the same thing (i.e. paraphrases) with only monolingual knowledge of the source language. Evaluations demonstrate that this approach can yield substantial improvements in translation quality.