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: after a shared CLWE space has been induced, the task is to retrieve target language translations for a test set of source language words retrieve target language translations for a test set of source language words BLI is cast as a ranking task in our BLI setup with only one correct translation for each “query” word, MAP is equal to mean reciprocal rank (MRR) 
 Question: How does BLI measure alignment quality?

Answer: we use mean average precision (MAP) as the main evaluation metric


Question: Results in Table TABREF13 show ten probes. Again, we see the performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks. 
 Question: For how many probe tasks the shallow-syntax-aware contextual embedding perform better than ELMo’s embedding?

Answer: performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks


Question: Fortunately, 180221 of 231162 author names could be matched successfully. There are many reasons for the remaining uncovered cases. 9073 Latin names could not be found in the name dictionary ENAMDICT and 14827 name matchings between the names' Latin and kanji representations did not succeed. These names might be missing at all in the dictionary, delivered in a very unusual format that the tool does not cover, or might not be Japanese or human names at all. Of course, Japanese computer scientists sometimes also cooperate with foreign colleagues but our tool expects Japanese names and is optimized for them. 
 Question: How successful are they at matching names of authors in Japanese and English?

Answer:
180221 of 231162 author names could be matched successfully