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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
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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)
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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)
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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)
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5
Can RNNs trained on harder subject-verb agreement instances still perform well on easier ones? ...
Bansal, Hritik
;
Bhatt, Gantavya
;
Agarwal, Sumeet
. - : arXiv, 2020
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6
How much complexity does an RNN architecture need to learn syntax-sensitive dependencies? ...
Bhatt, Gantavya
;
Bansal, Hritik
;
Singh, Rishubh
. - : arXiv, 2020
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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)
Abstract:
We used the Potsdam-Allahabad Hindi eye-tracking corpus to investigate the role of wordlevel and sentence-level factors during sentence comprehension in Hindi. Extending previous work that used this eye-tracking data, we investigate the role of surprisal and retrieval cost metrics during sentence processing. While controlling for word-level predictors (word complexity, syllable length, unigram and bigram frequencies) as well as sentence-level predictors such as integration and storage costs, we find a significant effect of surprisal on first-pass reading times (higher surprisal value leads to increase in FPRT). Effect of retrieval cost was only found for a higher degree of parser parallelism. Interestingly, while surprisal has a significant effect on FPRT, storage cost (another predictionbased metric) does not. A significant effect of storage cost shows up only in total fixation time (TFT), thus indicating that these two measures perhaps capture different aspects of prediction. The study replicates previous findings that both prediction-based and memorybased metrics are required to account for processing patterns during sentence comprehension. The results also show that parser model assumptions are critical in order to draw generalizations about the utility of a metric (e.g. surprisal) across various phenomena in a language.
Keyword:
Research Article
URL:
http://www.ncbi.nlm.nih.gov/pubmed/33828649
https://doi.org/10.16910/jemr.10.2.4
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141052/
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