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Span Selection Pre-training for Question Answering

This paper introduces a new pre-training task inspired by reading comprehension to better align the pre- training from memorization to understanding, and shows that the proposed model has strong empirical evidence as it obtains SOTA results on Natural Questions, a new benchmark MRC dataset, outperforming BERT-LARGE by 3 F1 points on short answer prediction.
ACL (opens in a new tab)

Translucent Answer Predictions in Multi-Hop Reading Comprehension

This paper proposes a novel deep neural architecture, called TAP (Translucent Answer Prediction), to identify answers and evidence (in the form of supporting facts) in an RCQA task requiring multi-hop reasoning.
Publisher (opens in a new tab)

Question Answering over Knowledge Bases by Leveraging Semantic Parsing and Neuro-Symbolic Reasoning

A semantic parsing and reasoning-based Neuro-Symbolic Question Answering system that achieves state-of-the-art performance on QALD-9 and LC-QuAD 1.0 and integrates multiple, reusable modules that are trained specifically for their individual tasks and do not require end-to-end training data.

Rank Prediction for Articles and Conference Papersusing Machine Learning Techniques

This paper gives the most accurate results of algorithms that can be used for rank predictions for articles using Machine Learning Techniques of Neural Networks, K-Means Algorithm which is a mixture of supervised and unsupervised learning for predicting ranks.
Publisher (opens in a new tab)
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