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SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative References
Evaluation of QA systems is very challenging and expensive, with the most reliable approach being human annotations of correctness of answers for questions.
Learning Answer Generation using Supervision from Automatic Question Answering Evaluators
Recent studies show that sentence-level extractive QA, i. e., based on Answer Sentence Selection (AS2), is outperformed by Generation-based QA (GenQA) models, which generate answers using the top-k answer sentences ranked by AS2 models (a la retrieval-augmented generation style).
Effective Pre-Training Objectives for Transformer-based Autoencoders
In this paper, we study trade-offs between efficiency, cost and accuracy when pre-training Transformer encoders with different pre-training objectives.
Knowledge Transfer from Answer Ranking to Answer Generation
In this paper, we propose to train a GenQA model by transferring knowledge from a trained AS2 model, to overcome the aforementioned issue.
Efficient pre-training objectives for Transformers
The Transformer architecture deeply changed the natural language processing, outperforming all previous state-of-the-art models.
