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Found 6 papers, 2 papers with code
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development
The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrievers and readers.
Synthetic Target Domain Supervision for Open Retrieval QA
Neural passage retrieval is a new and promising approach in open retrieval question answering.
Learning Cross-Lingual IR from an English Retriever
We present DR. DECR (Dense Retrieval with Distillation-Enhanced Cross-Lingual Representation), a new cross-lingual information retrieval (CLIR) system trained using multi-stage knowledge distillation (KD).
Cross-Lingual Information Retrieval
Knowledge Distillation
+3
End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training
End-to-end question answering (QA) requires both information retrieval (IR) over a large document collection and machine reading comprehension (MRC) on the retrieved passages.
Towards building a Robust Industry-scale Question Answering System
Building on top of the powerful BERTQA model, GAAMA provides a ∼2. 0{\%} absolute boost in F1 over the industry-scale state-of-the-art (SOTA) system on NQ.
A Canonical Architecture For Predictive Analytics on Longitudinal Patient Records
Many institutions within the healthcare ecosystem are making significant investments in AI technologies to optimize their business operations at lower cost with improved patient outcomes.
