no code implementations • COLING (CogALex) 2020 • Priyanka Sen
Speech disfluencies have been hypothesized to occur before words that are less predictable and therefore more cognitively demanding.
no code implementations • EMNLP (intexsempar) 2020 • Priyanka Sen, Emine Yilmaz
Collecting training data for semantic parsing is a time-consuming and expensive task.
1 code implementation • COLING 2022 • Priyanka Sen, Alham Fikri Aji, Amir Saffari
We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models.
no code implementations • EMNLP 2021 • Armin Oliya, Amir Saffari, Priyanka Sen, Tom Ayoola
Our model only needs the question text and the answer entities to train, and delivers a stand-alone QA model that does not require an additional ER component to be supplied during runtime.
1 code implementation • EMNLP 2021 • Priyanka Sen, Amir Saffari, Armin Oliya
End-to-end question answering using a differentiable knowledge graph is a promising technique that requires only weak supervision, produces interpretable results, and is fully differentiable.
no code implementations • EACL 2021 • Priyanka Sen, Isabel Groves
Speech disfluencies are prevalent in spontaneous speech.
2 code implementations • EMNLP 2020 • Priyanka Sen, Amir Saffari
While models have reached superhuman performance on popular question answering (QA) datasets such as SQuAD, they have yet to outperform humans on the task of question answering itself.