Papers by Sumeet Agarwal
How much complexity does an RNN architecture need to learn syntax-sensitive dependencies? (2020.acl-srw)
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| Challenge: | Long-term memory (LSTM) networks are capable of encapsulating long-range dependencies . but simple recurrent networks (SRNs) have been less successful at capturing long-term dependencies and loci of grammatical errors in an unsupervised setting. |
| Approach: | They propose a new architecture that incorporates the decaying nature of neuronal activations and models the excitatory and inhibitory connections in a population of neurons. |
| Outcome: | The proposed architecture shows competitive performance relative to LSTMs on subject-verb agreement, sentence grammaticality, and language modeling tasks. |
Dual Mechanism Priming Effects in Hindi Word Order (2022.aacl-main)
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| Challenge: | Existing studies have shown that word order choices can be primed by preceding sentences. |
| Approach: | They propose to model lexical priming and lexically-independent syntactic priming using a logistic regression model. |
| Outcome: | The proposed hypothesis supports multiple cognitive mechanisms . the experimental record shows that lexical priming and lexically-independent priming affect complementary sets of verb classes. |
Expectation and Locality Effects in the Prediction of Disfluent Fillers and Repairs in English Speech (N19-3)
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| Challenge: | a study aims to understand the role of disfluencies in speech production . speakers tend to lessen cognitive load for upcoming difficulties . |
| Approach: | They examine the role of three influential theories of language processing in predicting disfluencies in speech production. |
| Outcome: | The proposed classifiers predict disfluencies in English conversational speech . the classifier features lexical surprisal, word duration and DLT integration costs . |
Discourse Context Predictability Effects in Hindi Word Order (2022.emnlp-main)
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| Challenge: | Prior work has shown that information status, dependency length, and syntactic surprisal influence word order preferences, but the role of discourse predictability is underexplored in the literature. |
| Approach: | They propose to use Hindi-Urdu Treebank corpus to build a classifier to predict which sentences actually occurred in the corpus against artificially generated distractors. |
| Outcome: | The proposed classifier predicts which sentences occur in the Hindi-Urdu Treebank corpus against artificial distractors. |
SandhiKosh: A Benchmark Corpus for Evaluating Sanskrit Sandhi Tools (L18-1)
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| Challenge: | Several important texts which are of interest to people all over the world were written in Sanskrit. |
| Approach: | They develop a Sanskrit benchmark to evaluate the completeness and accuracy of tools . they use three most prominent tools to evaluate their completeness . |
| Outcome: | The proposed tools have substantial scope for improvement and are available to researchers worldwide. |