Aditya Bhargava


2021

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Proof Net Structure for Neural Lambek Categorial Parsing
Aditya Bhargava | Gerald Penn
Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)

In this paper, we present the first statistical parser for Lambek categorial grammar (LCG), a grammatical formalism for which the graphical proof method known as *proof nets* is applicable. Our parser incorporates proof net structure and constraints into a system based on self-attention networks via novel model elements. Our experiments on an English LCG corpus show that incorporating term graph structure is helpful to the model, improving both parsing accuracy and coverage. Moreover, we derive novel loss functions by expressing proof net constraints as differentiable functions of our model output, enabling us to train our parser without ground-truth derivations.

2020

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Supertagging with CCG primitives
Aditya Bhargava | Gerald Penn
Proceedings of the 5th Workshop on Representation Learning for NLP

In CCG and other highly lexicalized grammars, supertagging a sentence’s words with their lexical categories is a critical step for efficient parsing. Because of the high degree of lexicalization in these grammars, the lexical categories can be very complex. Existing approaches to supervised CCG supertagging treat the categories as atomic units, even when the categories are not simple; when they encounter words with categories unseen during training, their guesses are accordingly unsophisticated. In this paper, we make use of the primitives and operators that constitute the lexical categories of categorial grammars. Instead of opaque labels, we treat lexical categories themselves as linear sequences. We present an LSTM-based model that replaces standard word-level classification with prediction of a sequence of primitives, similarly to LSTM decoders. Our model obtains state-of-the-art word accuracy for single-task English CCG supertagging, increases parser coverage and F1, and is able to produce novel categories. Analysis shows a synergistic effect between this decomposed view and incorporation of prediction history.

2012

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Leveraging supplemental representations for sequential transduction
Aditya Bhargava | Grzegorz Kondrak
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2011

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How do you pronounce your name? Improving G2P with transliterations
Aditya Bhargava | Grzegorz Kondrak
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Leveraging Transliterations from Multiple Languages
Aditya Bhargava | Bradley Hauer | Grzegorz Kondrak
Proceedings of the 3rd Named Entities Workshop (NEWS 2011)

2010

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Transliteration Generation and Mining with Limited Training Resources
Sittichai Jiampojamarn | Kenneth Dwyer | Shane Bergsma | Aditya Bhargava | Qing Dou | Mi-Young Kim | Grzegorz Kondrak
Proceedings of the 2010 Named Entities Workshop

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Predicting the Semantic Compositionality of Prefix Verbs
Shane Bergsma | Aditya Bhargava | Hua He | Grzegorz Kondrak
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Language identification of names with SVMs
Aditya Bhargava | Grzegorz Kondrak
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

2009

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Multiple Word Alignment with Profile Hidden Markov Models
Aditya Bhargava | Grzegorz Kondrak
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium

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DirecTL: a Language Independent Approach to Transliteration
Sittichai Jiampojamarn | Aditya Bhargava | Qing Dou | Kenneth Dwyer | Grzegorz Kondrak
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)