In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
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Question: We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:

CNN-mean: taking the similarity score of the averaged feature of the two image sets.

CNN-avgmax: taking the average of the maximum similarity scores of two image sets.

Answer: Which vision-based approaches does this approach outperform?


Question: SIGHAN Bakeoff defines two types of evaluation settings, closed test limits all the data for learning should not be beyond the given training set, while open test does not take this limitation BIBREF21.

Answer: What is meant by closed test setting?


Question: 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.

Answer:
Why masking words in the decoder is helpful?