TASK DEFINITION: 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.
PROBLEM: We experiment and compare with the following models.

Pointer-Gen is the baseline model trained by optimizing $L_ ext{MLE}$ in Equation DISPLAY_FORM13.

Pointer-Gen+Pos is the baseline model by training Pointer-Gen only on positive examples whose sensationalism score is larger than 0.5

Pointer-Gen+Same-FT is the model which fine-tunes Pointer-Gen on the training samples whose sensationalism score is larger than 0.1

Pointer-Gen+Pos-FT is the model which fine-tunes Pointer-Gen on the training samples whose sensationalism score is larger than 0.5

Pointer-Gen+RL-ROUGE is the baseline model trained by optimizing $L_ ext{RL-ROUGE}$ in Equation DISPLAY_FORM17, with ROUGE-L BIBREF9 as the reward.

Pointer-Gen+RL-SEN is the baseline model trained by optimizing $L_ ext{RL-SEN}$ in Equation DISPLAY_FORM17, with $lpha _ ext{sen}$ as the reward.
Question: Which baselines are used for evaluation?

SOLUTION: Pointer-Gen Pointer-Gen+Pos Pointer-Gen+Same-FT Pointer-Gen+Pos-FT Pointer-Gen+RL-ROUGE Pointer-Gen+RL-SEN

PROBLEM: We propose a new unsupervised model, Sent2Vec, for learning universal sentence embeddings Formally, we learn a source (or context) embedding INLINEFORM0 and target embedding INLINEFORM1 for each word INLINEFORM2 in the vocabulary, with embedding dimension INLINEFORM3 and INLINEFORM4 as in ( EQREF6 ). The sentence embedding is defined as the average of the source word embeddings of its constituent words, as in ( EQREF8 ). We augment this model furthermore by also learning source embeddings for not only unigrams but also n-grams present in each sentence, and averaging the n-gram embeddings along with the words, i.e., the sentence embedding INLINEFORM5 for INLINEFORM6 is modeled as DISPLAYFORM0
Question: How do the n-gram features incorporate compositionality?

SOLUTION: by also learning source embeddings for not only unigrams but also n-grams present in each sentence, and averaging the n-gram embeddings along with the words

PROBLEM: The final results for our F1-score from this classification technique are 0.52 and 0.31 for the single and multi-turn data respectively.

We compare our results for both the single-turn and multi-turn experiments to the accuracies on the test data based off the BLEU score.
Question: which existing metrics do they compare with?

SOLUTION:
F1-score BLEU score