8 citations · 18 across the 14 of their papers we have counts for
8 papers · 1 filter
No Strong Feelings One Way or Another: Re-operationalizing Neutrality in Natural Language Inference
Animesh Nighojkar, Antonio Laverghetta, John Licato
Natural Language Inference (NLI) has been a cornerstone task in evaluating language models' inferential reasoning capabilities. However, the standard three-way classification schem…
Resolving Open-textured Rules with Templated Interpretive Arguments
John Licato, Logan Fields, Zaid Marji
Open-textured terms in written rules are typically settled through interpretive argumentation. Ongoing work has attempted to catalogue the schemes used in such interpretive argumen…
Cognitive Modeling of Semantic Fluency Using Transformers
Animesh Nighojkar, Anna Khlyzova, John Licato
Can deep language models be explanatory models of human cognition? If so, what are their limits? In order to explore this question, we propose an approach called hyperparameter hyp…
Predicting Human Psychometric Properties Using Computational Language Models
Antonio Laverghetta, Animesh Nighojkar, Jamshidbek Mirzakhalov +1
Transformer-based language models (LMs) continue to achieve state-of-the-art performance on natural language processing (NLP) benchmarks, including tasks designed to mimic human-in…
Developmental Negation Processing in Transformer Language Models
Antonio Laverghetta, John Licato
Reasoning using negation is known to be difficult for transformer-based language models. While previous studies have used the tools of psycholinguistics to probe a transformer's ab…
Evaluating Multiway Multilingual NMT in the Turkic Languages
Jamshidbek Mirzakhalov, Anoop Babu, Aigiz Kunafin +11
Despite the increasing number of large and comprehensive machine translation (MT) systems, evaluation of these methods in various languages has been restrained by the lack of high-…
Improving Paraphrase Detection with the Adversarial Paraphrasing Task
Animesh Nighojkar, John Licato
If two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, m…
Can Transformer Language Models Predict Psychometric Properties?
Antonio Laverghetta, Animesh Nighojkar, Jamshidbek Mirzakhalov +1
Transformer-based language models (LMs) continue to advance state-of-the-art performance on NLP benchmark tasks, including tasks designed to mimic human-inspired "commonsense" comp…