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.

Input: Consider Input: We conducted the human evaluation using Amazon Mechanical Turk to assess subjective quality. We recruit master level workers (who have good prior approval rates) to perform a human comparison between generated responses from two systems (which are randomly sampled from comparison systems). The workers are required to judge each utterance from 1 (bad) to 3 (good) in terms of informativeness and naturalness. Informativeness indicates the extent to which generated utterance contains all the information specified in the dialog act. Naturalness denotes whether the utterance is as natural as a human does. To reduce judgement bias, we distribute each question to three different workers. Finally, we collected in total of 5800 judges. 
 Question: What was the criteria for human evaluation?

Output: to judge each utterance from 1 (bad) to 3 (good) in terms of informativeness and naturalness


Input: Consider Input: We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets. To create a larger discrepancy between the tasks, so that there is a clear dataset size imbalance, the En-De data is artificially restricted to only 1 million parallel sentences, while the full En-Fr dataset, comprising almost 40 million parallel sentences, is used entirely.  
 Question: What datasets are used for experiments?

Output: the WMT'14 English-French (En-Fr) and English-German (En-De) datasets.


Input: Consider Input: We carried out experiments on four Chinese NLP tasks, including Emotion Classification (EC), Named Entity Recognition (NER), Sentence Pair Matching (SPM) and Natural Language Inference (NLI). 
 Question: What benchmarks did they experiment on?


Output: Emotion Classification (EC) Named Entity Recognition (NER) Sentence Pair Matching (SPM) Natural Language Inference (NLI)