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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 Abstract: Previous work suggests that RNNs trained on natural language corpora can capture number agreement well for simple sentences but perform less well when sentences contain agreement attractors: intervening nouns between the verb and the main subject with grammatical number opposite to the latter. This suggests these models may not learn the actual syntax of agreement, but rather infer shallower heuristics such as `agree with the recent noun'. In this work, we investigate RNN models with varying inductive biases trained on selectively chosen `hard' agreement instances, i.e., sentences with at least one agreement attractor. For these the verb number cannot be predicted using a simple linear heuristic, and hence they might help provide the model additional cues for hierarchical syntax. If RNNs can learn the underlying agreement rules when trained on such hard instances, then they should generalize well to other sentences, including simpler ones. However, we observe that several RNN types, including the ONLSTM ... : 15 pages, 3 figures, 13 Tables (including Appendix); Non Archival Extended Abstract Accepted in SciL 2021 - https://scholarworks.umass.edu/scil/vol4/iss1/38/ ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://arxiv.org/abs/2010.04976 https://dx.doi.org/10.48550/arxiv.2010.04976 BASE Hide 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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