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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) Abstract: The main subject and the associated verb in English must agree in grammatical number as per the Subject-Verb Agreement (SVA) phenomenon. It has been found that the presence of a noun between the verb and the main subject, whose grammatical number is opposite to that of the main subject, can cause speakers to produce a verb that agrees with the intervening noun rather than the main noun; the former thus acts as an agreement attractor. Such attractors have also been shown to pose a challenge for RNN models without explicit hierarchical bias to perform well on SVA tasks. Previous work suggests that syntactic cues in the input can aid such models to choose hierarchical rules over linear rules for number agreement. In this work, we investigate the effects of the choice of training data, training algorithm, and architecture on hierarchical generalization. We observe that the models under consideration fail to perform well on sentences with no agreement attractor when trained solely on natural sentences with at least one attractor. Even in the presence of this biased training set, implicit hierarchical bias in the architecture (as in the Ordered Neurons LSTM) is not enough to capture syntax-sensitive dependencies. These results suggest that current RNNs do not capture the underlying hierarchical rules of natural language, but rather use shallower heuristics for their predictions. Keyword: Artificial Intelligence and Robotics; Computational Linguistics; generalisation; Psycholinguistics and Neurolinguistics; RNN models; subject-verb agreement URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1228&context=scil https://scholarworks.umass.edu/scil/vol4/iss1/38 BASE Hide 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. - : arXiv, 2020 BASE Show 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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