Akash Bharadwaj


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To Test Machine Comprehension, Start by Defining Comprehension
Jesse Dunietz | Greg Burnham | Akash Bharadwaj | Owen Rambow | Jennifer Chu-Carroll | Dave Ferrucci
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Many tasks aim to measure machine reading comprehension (MRC), often focusing on question types presumed to be difficult. Rarely, however, do task designers start by considering what systems should in fact comprehend. In this paper we make two key contributions. First, we argue that existing approaches do not adequately define comprehension; they are too unsystematic about what content is tested. Second, we present a detailed definition of comprehension—a “Template of Understanding”—for a widely useful class of texts, namely short narratives. We then conduct an experiment that strongly suggests existing systems are not up to the task of narrative understanding as we define it.


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Phonologically Aware Neural Model for Named Entity Recognition in Low Resource Transfer Settings
Akash Bharadwaj | David Mortensen | Chris Dyer | Jaime Carbonell
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors
David R. Mortensen | Patrick Littell | Akash Bharadwaj | Kartik Goyal | Chris Dyer | Lori Levin
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This paper contributes to a growing body of evidence that—when coupled with appropriate machine-learning techniques–linguistically motivated, information-rich representations can outperform one-hot encodings of linguistic data. In particular, we show that phonological features outperform character-based models. PanPhon is a database relating over 5,000 IPA segments to 21 subsegmental articulatory features. We show that this database boosts performance in various NER-related tasks. Phonologically aware, neural CRF models built on PanPhon features are able to perform better on monolingual Spanish and Turkish NER tasks that character-based models. They have also been shown to work well in transfer models (as between Uzbek and Turkish). PanPhon features also contribute measurably to Orthography-to-IPA conversion tasks.