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.

[Q]: We evaluated our models on 3 different datasets:

CSAT dataset for CSAT prediction, consisting of spoken transcripts (automatic via ASR).

20 newsgroups for topic identification task, consisting of written text;

Fisher Phase 1 corpus for topic identification task, consisting of spoken transcripts (manual);
Question: What datasets did they use for evaluation?
[A]: CSAT dataset 20 newsgroups Fisher Phase 1 corpus

[Q]: We carry out experiments on multiple language pairs including German-English, French-English, and Japanese-English.
Question: Which languages are used in the multi-lingual caption model?
[A]: German-English, French-English, and Japanese-English

[Q]: It should also be noted that signals can be recognized even when the model misclassifies relations, since ${\Delta }_s$ does not rely on correct classification: it merely quantifies the contribution of a word in context toward the correct label's score. If we examine the influence of each word on the score of the correct relation, that impact should and does still correlate with human judgments based on what the system may tag as the second or third best class to choose.
Question: Where does proposed metric overlap with juman judgement?
[A]:
influence of each word on the score of the correct relation, that impact should and does still correlate with human judgments