In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

[Q]: We illustrate the concept by discussing some instantiations that we have recently experimented with. The design also provides for a clear "relevance place" at which an opportunity arises for semantic negotiation, namely, before the final decision is made. An example of this is shown in the example below. As SECREF12 shows, even in cases where a decision can be reached quickly, there can be an explicit mutual confirmation step, before the (silent) decision signal is sent. A third setting that we have explored BIBREF19 brings conceptual negotiation more clearly into the foreground. In that game, the players are presented with images of birds of particular species and are tasked with coming up with a description of common properties. Again, the final answer has to be approved by both participants. As SECREF13 shows, this can lead to an explicit negotiation of conceptual content.
Question: Do the authors perform experiments using their proposed method?
[A]: Yes-no

[Q]: 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.
Question: What transfer learning tasks are evaluated?
[A]: Extractive

[Q]: For a word belonging to any one of the word-groups representing these concepts, the modified cost term favors an increase for the value of this word's embedding vector dimension corresponding to the concept that the particular word belongs to.
Question: Along which dimension do the semantically related words take larger values?
[A]:
Extractive