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.
Example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.
Question: Are results reported only for English data?
Example solution: No
Example explanation: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

Problem: Table TABREF16 shows the confusion matrices for facial and audio emotion recognition on our complete AMMER data set and Table TABREF17 shows the results per class for each method, including facial and audio data and micro and macro averages. The classification from facial expressions yields a macro-averaged $ ext{F}_1$ score of 33 % across the three emotions joy, insecurity, and annoyance (P=0.31, R=0.35).
Question: How is face and audio data analysis evaluated?
Solution: confusion matrices $ ext{F}_1$ score