Search Results for author: Animesh Nighojkar

Found 6 papers, 3 papers with code

No Strong Feelings One Way or Another: Re-operationalizing Neutrality in Natural Language Inference

no code implementations16 Jun 2023 Animesh Nighojkar, Antonio Laverghetta Jr., John Licato

Natural Language Inference (NLI) has been a cornerstone task in evaluating language models' inferential reasoning capabilities.

Natural Language Inference

Cognitive Modeling of Semantic Fluency Using Transformers

no code implementations20 Aug 2022 Animesh Nighojkar, Anna Khlyzova, John Licato

We report preliminary evidence suggesting that, despite obvious implementational differences in how people and TLMs learn and use language, TLMs can be used to identify individual differences in human fluency task behaviors better than existing computational models, and may offer insights into human memory retrieval strategies -- cognitive process not typically considered to be the kinds of things TLMs can model.


Predicting Human Psychometric Properties Using Computational Language Models

no code implementations12 May 2022 Antonio Laverghetta Jr., Animesh Nighojkar, Jamshidbek Mirzakhalov, John Licato

In other words, can LMs be of use in predicting the psychometric properties of test items, when those items are given to human participants?

Improving Paraphrase Detection with the Adversarial Paraphrasing Task

1 code implementation ACL 2021 Animesh Nighojkar, John Licato

Can we teach them instead to identify paraphrases in a way that draws on the inferential properties of the sentences, and is not over-reliant on lexical and syntactic similarities of a sentence pair?

Paraphrase Identification Sentence

Can Transformer Language Models Predict Psychometric Properties?

1 code implementation Joint Conference on Lexical and Computational Semantics 2021 Antonio Laverghetta Jr., Animesh Nighojkar, Jamshidbek Mirzakhalov, John Licato

We then use the responses to calculate standard psychometric properties of the items in the diagnostic test, using the human responses and the LM responses separately.

Probing the Natural Language Inference Task with Automated Reasoning Tools

1 code implementation6 May 2020 Zaid Marji, Animesh Nighojkar, John Licato

The Natural Language Inference (NLI) task is an important task in modern NLP, as it asks a broad question to which many other tasks may be reducible: Given a pair of sentences, does the first entail the second?

Natural Language Inference

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