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.

Input: Consider Input: 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.

Output: What transfer learning tasks are evaluated?


Input: Consider Input: Unsupervised Evaluation
The unsupervised tasks include five tasks from SemEval Semantic Textual Similarity (STS) in 2012-2016 BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 and the SemEval2014 Semantic Relatedness task (SICK-R) BIBREF35 .

The cosine similarity between vector representations of two sentences determines the textual similarity of two sentences, and the performance is reported in Pearson's correlation score between human-annotated labels and the model predictions on each dataset.

Supervised Evaluation
It includes Semantic relatedness (SICK) BIBREF35 , SemEval (STS-B) BIBREF36 , paraphrase detection (MRPC) BIBREF37 , question-type classification (TREC) BIBREF38 , movie review sentiment (MR) BIBREF39 , Stanford Sentiment Treebank (SST) BIBREF40 , customer product reviews (CR) BIBREF41 , subjectivity/objectivity classification (SUBJ) BIBREF42 , opinion polarity (MPQA) BIBREF43 .

Output: How do they evaluate the sentence representations?


Input: Consider Input: These sentiment scores were automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15 and pretrained on in-house psychiatric EHR text.


Output: How do they extract topics?