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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 Linguistic Complexity and Planning Effects on Word Duration in Hindi Read Aloud Speech ... Ranjan, Sidharth; Rajkumar, Rajakrishnan; Agarwal, Sumeet. - : University of Massachusetts Amherst, 2022 BASE Show details 2 Linguistic Complexity and Planning Effects on Word Duration in Hindi Read Aloud Speech Ranjan, Sidharth; Rajkumar, Rajakrishnan; Agarwal, Sumeet In: Proceedings of the Society for Computation in Linguistics (2022) BASE Show details 3 Can RNNs trained on harder subject-verb agreement instances still perform well on easier ones? Bansal, Hritik; Bhatt, Gantavya; Agarwal, Sumeet In: Proceedings of the Society for Computation in Linguistics (2021) BASE Show details 4 Effects of Duration, Locality, and Surprisal in Speech Disfluency Prediction in English Spontaneous Speech Rajkumar, Rajakrishnan; Agarwal, Sumeet; Dammalapati, Samvit In: Proceedings of the Society for Computation in Linguistics (2021) BASE Show details 5 Can RNNs trained on harder subject-verb agreement instances still perform well on easier ones? ... Bansal, Hritik; Bhatt, Gantavya; Agarwal, Sumeet. - : arXiv, 2020 BASE Show details 6 How much complexity does an RNN architecture need to learn syntax-sensitive dependencies? ... Bhatt, Gantavya; Bansal, Hritik; Singh, Rishubh; Agarwal, Sumeet. - : arXiv, 2020 Abstract: Long short-term memory (LSTM) networks and their variants are capable of encapsulating long-range dependencies, which is evident from their performance on a variety of linguistic tasks. On the other hand, simple recurrent networks (SRNs), which appear more biologically grounded in terms of synaptic connections, have generally been less successful at capturing long-range dependencies as well as the loci of grammatical errors in an unsupervised setting. In this paper, we seek to develop models that bridge the gap between biological plausibility and linguistic competence. We propose a new architecture, the Decay RNN, which incorporates the decaying nature of neuronal activations and models the excitatory and inhibitory connections in a population of neurons. Besides its biological inspiration, our model also shows competitive performance relative to LSTMs on subject-verb agreement, sentence grammaticality, and language modeling tasks. These results provide some pointers towards probing the nature of the ... : 11 pages, 5 figures (including appendix); to appear at ACL SRW 2020 ... Keyword: Computation and Language cs.CL; FOS Biological sciences; FOS Computer and information sciences; I.2.6; I.2.7; J.5; Neurons and Cognition q-bio.NC URL: https://dx.doi.org/10.48550/arxiv.2005.08199 https://arxiv.org/abs/2005.08199 BASE Hide details 7 Role of Expectation and Working Memory Constraints in Hindi Comprehension: An Eye-tracking Corpus Analysis Agrawal, Arpit; Agarwal, Sumeet; Husain, Samar In: J Eye Mov Res (2017) BASE Show details

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