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CLER: Cross task Learning with Expert Representation to Generalize Reading and Understanding

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Figure

Table 1: Characteristics of released datasets for theMRQA shared task. The top part of the table indicatesin-domain datasets to train and validate the model, andthe bottom part of the table indicates unveiled out-of-domain datasets to validate the generalization of thetrained model.
Figure 2: Architecture of the MoE layer. ⊗ representsthe multiplication operator, and ⊕ represents the sum-mation operator.
Table 2: Statistics of datasets for RC tasks. The toppart of the table indicates in-domain datasets to trainand validate the model, and the bottom part of the ta-ble indicates unveiled out-of-domain datasets to onlyvalidate the trained model.
Table 4: Results on the in-domain development set. Bold values indicate the best scores overall, and the underlinedvalues indicate the best scores for each single model

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