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.
--------
Question: As shown in Table , the proposed PS-rnn-elmo shows a significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute).
Question: How much better performance of proposed model compared to answer-selection models?

Answer: significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute)

Question: In our experiments we use the same data-sets from other works BIBREF32, BIBREF23, BIBREF33 as well as other datasets that we collected by us using a similar criterion (described in Section SECREF4). We use thirty different discussions that took place between March 2015 and June 2019, half of them with controversy and half without it. We considered discussions in four different languages: English, Portuguese, Spanish and French, occurring in five regions over the world: South and North America, Western Europe, Central and Southern Asia. We also studied these discussions taking first 140 characters and then 280 from each tweet to analyze the difference in performance and computing time wrt the length of the posts.
Question: What datasets did they use?

Answer: BIBREF32, BIBREF23, BIBREF33 discussions in four different languages: English, Portuguese, Spanish and French, occurring in five regions over the world: South and North America, Western Europe, Central and Southern Asia.

Question: Although such an approach has been used in different studies during feature engineering, the selection of word vectors and the number of clusters remain a trial-end-error procedure.
Question: Which other hyperparameters, other than number of clusters are typically evaluated in this type of research?

Answer:
selection of word vectors