In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
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Question: To test the robustness of the taggers against the OOV problem, we also conduct experiments using artificially constructed unnormalized text by corrupting words in the normal dev set. Again, the CNN tagger outperforms the two baselines by a very large margin.
Question: How do they confirm their model working well on out-of-vocabulary problems?

Answer: conduct experiments using artificially constructed unnormalized text by corrupting words in the normal dev set

Question: If we take the best psr ensemble trained on CBT as a baseline, improving the model architecture as in BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , continuing to use the original CBT training data, lead to improvements of INLINEFORM0 and INLINEFORM1 absolute on named entities and common nouns respectively. By contrast, inflating the training dataset provided a boost of INLINEFORM2 while using the same model.
Question: How large are the improvements of the Attention-Sum Reader model when using the BookTest dataset?

Answer: INLINEFORM2

Question: For the Russian language, with its rich morphology, lemmatizing the training and testing data for ELMo representations yields small but consistent improvements in the WSD task.
Question: What other examples of morphologically-rich languages do the authors give?

Answer:
Russian