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 help solve the data problem we present Taskmaster-1, a dataset consisting of 13,215 dialogs, including 5,507 spoken and 7,708 written dialogs created with two distinct procedures. Each conversation falls into one of six domains: ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations.  
 Question: Which six domains are covered in the dataset?

Answer: ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations


Question: In Table TABREF1, we summarize the quantitative results of the above previous studies. It includes the model basis, feature description, languages classified and the used dataset along with accuracy obtained. The table also lists the overall results of our proposed models (at the top). The languages used by various authors along with their acronyms are English (En), Spanish (Es), French (Fr), German (De), Russian (Ru), Italian (It), Bengali (Ben), Hindi (Hi) and Telegu (Tel). 
 Question: Is the performance compared against a baseline model?

Answer: Yes


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