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 the SBERT sentence embeddings to other sentence embeddings methods on the following seven SentEval transfer tasks:

MR: Sentiment prediction for movie reviews snippets on a five start scale BIBREF25.

CR: Sentiment prediction of customer product reviews BIBREF26.

SUBJ: Subjectivity prediction of sentences from movie reviews and plot summaries BIBREF27.

MPQA: Phrase level opinion polarity classification from newswire BIBREF28.

SST: Stanford Sentiment Treebank with binary labels BIBREF29.

TREC: Fine grained question-type classification from TREC BIBREF30.

MRPC: Microsoft Research Paraphrase Corpus from parallel news sources BIBREF31.

Answer: What transfer learning tasks are evaluated?


Question: Thus we propose two kinds of relation-specific meta information: relation meta and gradient meta corresponding to afore mentioned two perspectives respectively. In our proposed framework MetaR, relation meta is the high-order representation of a relation connecting head and tail entities. Gradient meta is the loss gradient of relation meta which will be used to make a rapid update before transferring relation meta to incomplete triples during prediction.

Answer: What meta-information is being transferred?


Question:  For a fair comparison with previous work, we train the baseline MT system on the data released by BIBREF11. 

Answer:
what was the baseline?