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Latent Retrieval for Weakly Supervised Open Domain Question Answering

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Figure

Figure 1: Overview of ORQA. A subset of all possible answer derivations given a question qblocks are jointly encoded with the question, and span representations are scored with a multi-layer perceptron(MLP) to computeRetrieval scores is shown here
Table 2: Examples of spurious ambiguities arising fromthe use of weak supervision. Good evidence retrieval isneeded to generate a meaningful learning signal.
Table 4:A breakdown of biases in existing QAdatasets. These biases are associated with either thequestion or the answer.
Figure 3: Analysis: Performance on our open versionof the Natural Questions dev set with various mask-ing rates for the ICT pre-training

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