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.

[EX Q]: Experiments ::: Baselines
For comparison, we select several public models as baselines including semantic parsing models: BiDAF BIBREF3, an MRC model which utilizes a bi-directional attention flow network to encode the question and passage;

QANet BIBREF12, which utilizes convolutions and self-attentions as the building blocks of encoders to represent the question and passage;

BERT BIBREF23, a pre-trained bidirectional Transformer-based language model which achieves state-of-the-art performance on lots of public MRC datasets recently;

and numerical MRC models: NAQANet BIBREF6, a numerical version of QANet model.

NAQANet+, an enhanced version of NAQANet implemented by ourselves, which further considers real number (e.g. "2.5"), richer arithmetic expression, data augmentation, etc. Syn Dep BIBREF6, the neural semantic parsing model (KDG) BIBREF22 with Stanford dependencies based sentence representations;

OpenIE BIBREF6, KDG with open information extraction based sentence representations;

SRL BIBREF6, KDG with semantic role labeling based sentence representations;

and traditional MRC models:
Question: what are the existing models they compared with?
[EX A]: Syn Dep OpenIE SRL BiDAF QANet BERT NAQANet NAQANet+

[EX Q]: We modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions.

Results showed that users who, on average, produced utterances with more words gave significantly higher ratings ($eta $=0.01, SE=0.002, t=4.79, p$<$0.001)(see Figure 2) and engaged with Gunrock for significantly greater number of turns ($eta $=1.85, SE=0.05, t=35.58, p$<$0.001) (see Figure 2). These results can be interpreted as evidence for Gunrock's ability to handle complex sentences, where users are not constrained to simple responses to be understood and feel engaged in the conversation and evidence that individuals are more satisfied with the conversation when they take a more active role, rather than the system dominating the dialog. On the other hand, another interpretation is that users who are more talkative may enjoy talking to the bot in general, and thus give higher ratings in tandem with higher average word counts.
Question: How do they correlate user backstory queries to user satisfaction?
[EX A]: modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions

[EX Q]: We evaluated the phrase compositionality models on the adjectivenoun and nounnoun phrase similarity tasks compiled by Mitchell2010, using the same evaluation scheme as in the original work. Spearman's INLINEFORM0 between phrasal similarities derived from our compositional functions and the human annotators (computed individually per annotator and then averaged across all annotators) was the evaluation measure.
Question: How do they score phrasal compositionality?
[EX A]:
Spearman's INLINEFORM0 between phrasal similarities derived from our compositional functions and the human annotators