TASK DEFINITION: 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.
PROBLEM: We design the word-level refine decoder because this process is similar to the cloze task in BERT's pre-train process, therefore by using the ability of the contextual language model the decoder can generate more fluent and natural sequences.
Question: Why masking words in the decoder is helpful?

SOLUTION: ecause this process is similar to the cloze task in BERT's pre-train process, therefore by using the ability of the contextual language model the decoder can generate more fluent and natural sequences.

PROBLEM: 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?

SOLUTION: $150,000$ tweets

PROBLEM: We treat each individual language as a task and train a joint model for all the tasks. We seek to learn a set of shared character embeddings for taggers in both languages together through optimization of a joint loss function that combines the high-resource tagger and the low-resource one.
Question: How are character representations from various languages joint?

SOLUTION:
shared character embeddings for taggers in both languages together through optimization of a joint loss function