47 papers with code • 1 benchmarks • 1 datasets
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We introduce an approach for open-domain question answering (QA) that retrieves and reads a passage graph, where vertices are passages of text and edges represent relationships that are derived from an external knowledge base or co-occurrence in the same article.
Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge.
Additionally, we show that the transfer step of TANDA makes the adaptation step more robust to noise.
Progress in cross-lingual modeling depends on challenging, realistic, and diverse evaluation sets.
We also explore two approaches for end-to-end supervised training of the reader and retriever components in OpenQA models.