1 code implementation • EMNLP 2021 • Jamshidbek Mirzakhalov, Anoop Babu, Duygu Ataman, Sherzod Kariev, Francis Tyers, Otabek Abduraufov, Mammad Hajili, Sardana Ivanova, Abror Khaytbaev, Antonio Laverghetta Jr., Bekhzodbek Moydinboyev, Esra Onal, Shaxnoza Pulatova, Ahsan Wahab, Orhan Firat, Sriram Chellappan
Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems.
no code implementations • 16 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.
no code implementations • 12 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?
1 code implementation • ACL 2022 • Antonio Laverghetta Jr., John Licato
Reasoning using negation is known to be difficult for transformer-based language models.
no code implementations • 9 Sep 2021 • Jamshidbek Mirzakhalov, Anoop Babu, Duygu Ataman, Sherzod Kariev, Francis Tyers, Otabek Abduraufov, Mammad Hajili, Sardana Ivanova, Abror Khaytbaev, Antonio Laverghetta Jr., Behzodbek Moydinboyev, Esra Onal, Shaxnoza Pulatova, Ahsan Wahab, Orhan Firat, Sriram Chellappan
Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems.
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.
1 code implementation • Asian Chapter of the Association for Computational Linguistics 2020 • Antonio Laverghetta Jr., Jamshidbek Mirzakhalov, John Licato
Curriculum learning, a training strategy where training data are ordered based on their difficulty, has been shown to improve performance and reduce training time on various NLP tasks.