NewsQA
Related Papers
Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
AbstractWe develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network. Given a high performing MC model in one domain, our technique aims to …...
Harvesting and Refining Question-Answer Pairs for Unsupervised QA
AbstractQuestion Answering (QA) has shown great success thanks to the availability of large-scale datasets and the effectiveness of neural models. Recent research works have attempted to extend these successes to …...
Robust Machine Reading Comprehension by Learning Soft labels
AbstractNeural models have achieved great success on the task of machine reading comprehension (MRC), which are typically trained on hard labels. We argue that hard labels limit the model capability …...
Undersensitivity in Neural Reading Comprehension
AbstractCurrent reading comprehension methods generalise well to in-distribution test sets, yet perform poorly on adversarially selected data. Prior work on adversarial inputs typically studies model oversensitivity: semantically invariant text perturbations …...
Keep Learning: Self-supervised Meta-learning for Learning from Inference
AbstractA common approach in many machine learning algorithms involves self-supervised learning on large unlabeled data before fine-tuning on downstream tasks to further improve performance. A new approach for language modelling, …...
Learning to Generate Questions by Learning to Recover Answer-containing Sentences
To train a question answering model based on machine reading comprehension (MRC), sig- nificant effort is required to prepare annotated training data composed of questions and their answers from contexts. …...
Unsupervised Question Answering via Answer Diversifying
AbstractUnsupervised question answering is an attractive task due to its independence on labeled data. Previous works usually make use of heuristic rules as well as pre-trained models to construct data …...