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: The training objective is to minimize the negative likelihood of the aligned unanswerable question $ ilde{q}$ given the answerable question $q$ and its corresponding paragraph $p$ that contains the answer $a$ : L=-(q,q,p,a)DP(q|q,p,a;) where $\mathcal {D}$ is the training corpus and $ heta $ denotes all the parameters. Sequence-to-sequence and pair-to-sequence models are trained with the same objective.

Answer: What is the training objective of their pair-to-sequence model?

Question: In this paper, we use three data sets from the literature to train and evaluate our own classifier. Data collected by BIBREF3 , which we term the Sexist/Racist (SR) data set, was collected using an initial Twitter search followed by analysis and filtering by the authors and their team who identified 17 common phrases, hashtags, and users that were indicative of abusive speech. BIBREF4 collected the HATE dataset by searching for tweets using a lexicon provided by Hatebase.org. The final data set we used, which we call HAR, was collected by BIBREF9 ; we removed all retweets reducing the dataset to 20,000 tweets. Tweets were labeled as "Harrassing" or "Non-Harrassing"; hate speech was not explicitly labeled, but treated as an unlabeled subset of the broader "Harrassing" category BIBREF9 .

Answer: Which publicly available datasets are used?

Question: The average content score across the paintings is 3.7 which demonstrates that the prose generated is relevant to the painting. The average creativity score is 3.9 which demonstrates that the model captures more than basic objects in the painting successfully using poetic clues in the scene. The average style score is 3.9 which demonstrates that the prose generated is perceived to be in the style of Shakespeare.

Answer:
How does final model rate on Likert scale?