Ajay Patel


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TopGuNN: Fast NLP Training Data Augmentation using Large Corpora
Rebecca Iglesias-Flores | Megha Mishra | Ajay Patel | Akanksha Malhotra | Reno Kriz | Martha Palmer | Chris Callison-Burch
Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances

Acquiring training data for natural language processing systems can be expensive and time-consuming. Given a few training examples crafted by experts, large corpora can be mined for thousands of semantically similar examples that provide useful variability to improve model generalization. We present TopGuNN, a fast contextualized k-NN retrieval system that can efficiently index and search over contextual embeddings generated from large corpora. TopGuNN is demonstrated for a training data augmentation use case over the Gigaword corpus. Using approximate k-NN and an efficient architecture, TopGuNN performs queries over an embedding space of 4.63TB (approximately 1.5B embeddings) in less than a day.


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Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package
Ajay Patel | Alexander Sands | Chris Callison-Burch | Marianna Apidianaki
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Vector space embedding models like word2vec, GloVe, and fastText are extremely popular representations in natural language processing (NLP) applications. We present Magnitude, a fast, lightweight tool for utilizing and processing embeddings. Magnitude is an open source Python package with a compact vector storage file format that allows for efficient manipulation of huge numbers of embeddings. Magnitude performs common operations up to 60 to 6,000 times faster than Gensim. Magnitude introduces several novel features for improved robustness like out-of-vocabulary lookups.