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: Twitter data: We used the Twitter API to scrap tweets with hashtags. All non-English tweets were filtered out by the API.
Question: Do they evaluate only on English datasets?

A: Yes
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Q: Probing into the assorted empirical results may help us discover some interesting phenomenons:

The advantage of pre-training gradually diminishes with the increase of labeled data BIBREF14, BIBREF17, BIBREF18.

Fixed representations yield better results than fine-tuning in some cases BIBREF24.

Overall, pre-training the Seq2Seq encoder outperforms pre-training the decoder BIBREF24, BIBREF17, BIBREF15, BIBREF16.
Question: What experimental phenomena are presented?

A: The advantage of pre-training gradually diminishes with the increase of labeled data Fixed representations yield better results than fine-tuning in some cases pre-training the Seq2Seq encoder outperforms pre-training the decoder
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Q: As BIBREF0 discussed, the text-only UMT is fundamentally an ill-posed problem, since there are potentially many ways to associate target with source sentences. Intuitively, since the visual content and language are closely related, the image can play the role of a pivot "language" to bridge the two languages without paralleled corpus, making the problem "more well-defined" by reducing the problem to supervised learning.
Question: Why is this work different from text-only UNMT?

A:
the image can play the role of a pivot "language" to bridge the two languages without paralleled corpus
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