Bahadorreza Ofoghi


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PIE-QG: Paraphrased Information Extraction for Unsupervised Question Generation from Small Corpora
Dinesh Nagumothu | Bahadorreza Ofoghi | Guangyan Huang | Peter Eklund
Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)

Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome. Our approach (called PIE-QG) uses Open Information Extraction (OpenIE) to generate synthetic training questions from paraphrased passages and uses the question-answer pairs as training data for a language model for a state-of-the-art QA system based on BERT. Triples in the form of <subject, predicate, object> are extracted from each passage, and questions are formed with subjects (or objects) and predicates while objects (or subjects) are considered as answers. Experimenting on five extractive QA datasets demonstrates that our technique achieves on-par performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources.


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Syndromic Surveillance through Measuring Lexical Shift in Emergency Department Chief Complaint Texts
Hafsah Aamer | Bahadorreza Ofoghi | Karin Verspoor
Proceedings of the Australasian Language Technology Association Workshop 2016


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From Lexical Entailment to Recognizing Textual Entailment Using Linguistic Resources
Bahadorreza Ofoghi | John Yearwood
Proceedings of the Australasian Language Technology Association Workshop 2009


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Two-Step Comprehensive Open Domain Text Annotation with Frame Semantics
Bahadorreza Ofoghi | John Yearwood | Liping Ma
Proceedings of the Australasian Language Technology Workshop 2007