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.

[EX Q]: We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps. 
 Question: How many tweats does MMHS150k contains, 150000?
[EX A]: $150,000$ tweets

[EX Q]: We evaluate the generations with the ROUGE BIBREF29 and METEOR BIBREF30 metrics using the true sentences as targets. 
 Question: What metrics are used for evaluation?
[EX A]: ROUGE BIBREF29 and METEOR BIBREF30

[EX Q]: We adopt several standard metrics widely used in existing works to measure the performance of dialogue generation models, including BLEU BIBREF24, embedding-based metrics (Average, Extrema, Greedy and Coherence) BIBREF25, BIBREF26, entropy-based metrics (Ent-{1,2}) BIBREF0 and distinct metrics (Dist-{1,2,3} and Intra-{1,2,3}) BIBREF1, BIBREF6. 
 Question: What automatic evaluation metrics are used?
[EX A]:
BLEU embedding-based metrics (Average, Extrema, Greedy and Coherence) , entropy-based metrics (Ent-{1,2}) distinct metrics (Dist-{1,2,3} and Intra-{1,2,3})