no code implementations • EMNLP (FEVER) 2021 • Aalok Sathe, Joonsuk Park
Automatic fact-checking is crucial for recognizing misinformation spreading on the internet.
no code implementations • NAACL (ACL) 2022 • Greta Tuckute, Aalok Sathe, Mingye Wang, Harley Yoder, Cory Shain, Evelina Fedorenko
The modular design of SentSpace allows researchersto easily integrate their own feature computation into the pipeline while benefiting from acommon framework for evaluation and visualization.
1 code implementation • EMNLP (MRL) 2021 • Karthikeyan K, Aalok Sathe, Somak Aditya, Monojit Choudhury
Multilingual language models achieve impressive zero-shot accuracies in many languages in complex tasks such as Natural Language Inference (NLI).
1 code implementation • ACL (SIGMORPHON) 2021 • Saujas Vaduguru, Aalok Sathe, Monojit Choudhury, Dipti Misra Sharma
Neural models excel at extracting statistical patterns from large amounts of data, but struggle to learn patterns or reason about language from only a few examples.
1 code implementation • CONLL 2020 • Pratik Joshi, Somak Aditya, Aalok Sathe, Monojit Choudhury
Pre-trained Transformer-based neural architectures have consistently achieved state-of-the-art performance in the Natural Language Inference (NLI) task.
no code implementations • LREC 2020 • Aalok Sathe, Salar Ather, Tuan Manh Le, Nathan Perry, Joonsuk Park
However, such datasets suffer from limited applicability due to the synthetic nature of claims and/or evidence written by annotators that differ from real claims and evidence on the internet.