Patricia Thaine


2022

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Proceedings of the Fourth Workshop on Privacy in Natural Language Processing
Oluwaseyi Feyisetan | Sepideh Ghanavati | Patricia Thaine | Ivan Habernal | Fatemehsadat Mireshghallah
Proceedings of the Fourth Workshop on Privacy in Natural Language Processing

2021

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The Chinese Remainder Theorem for Compact, Task-Precise, Efficient and Secure Word Embeddings
Patricia Thaine | Gerald Penn
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

The growing availability of powerful mobile devices and other edge devices, together with increasing regulatory and security concerns about the exchange of personal information across networks of these devices has challenged the Computational Linguistics community to develop methods that are at once fast, space-efficient, accurate and amenable to secure encoding schemes such as homomorphic encryption. Inspired by recent work that restricts floating point precision to speed up neural network training in hardware-based SIMD, we have developed a method for compressing word vector embeddings into integers using the Chinese Reminder Theorem that speeds up addition by up to 48.27% and at the same time compresses GloVe word embedding libraries by up to 25.86%. We explore the practicality of this simple approach by investigating the trade-off between precision and performance in two NLP tasks: compositional semantic relatedness and opinion target sentiment classification. We find that in both tasks, lowering floating point number precision results in negligible changes to performance.

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Proceedings of the Third Workshop on Privacy in Natural Language Processing
Oluwaseyi Feyisetan | Sepideh Ghanavati | Shervin Malmasi | Patricia Thaine
Proceedings of the Third Workshop on Privacy in Natural Language Processing

2020

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Proceedings of the Second Workshop on Privacy in NLP
Oluwaseyi Feyisetan | Sepideh Ghanavati | Shervin Malmasi | Patricia Thaine
Proceedings of the Second Workshop on Privacy in NLP

2017

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Vowel and Consonant Classification through Spectral Decomposition
Patricia Thaine | Gerald Penn
Proceedings of the First Workshop on Subword and Character Level Models in NLP

We consider two related problems in this paper. Given an undeciphered alphabetic writing system or mono-alphabetic cipher, determine: (1) which of its letters are vowels and which are consonants; and (2) whether the writing system is a vocalic alphabet or an abjad. We are able to show that a very simple spectral decomposition based on character co-occurrences provides nearly perfect performance with respect to answering both question types.