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]: SVM: We define 3 sets of features to characterize each question. The first is a simple bag-of-words set of features over the question (SVM-BOW), the second is bag-of-words features of the question as well as length of the question in words (SVM-BOW + LEN), and lastly we extract bag-of-words features, length of the question in words as well as part-of-speech tags for the question (SVM-BOW + LEN + POS). This results in vectors of 200, 201 and 228 dimensions respectively, which are provided to an SVM with a linear kernel. No-Answer Baseline (NA) : Most of the questions we receive are difficult to answer in a legally-sound way on the basis of information present in the privacy policy. We establish a simple baseline to quantify the effect of identifying every question as unanswerable. Word Count Baseline : To quantify the effect of using simple lexical matching to answer the questions, we retrieve the top candidate policy sentences for each question using a word count baseline BIBREF53, which counts the number of question words that also appear in a sentence. We include the top 2, 3 and 5 candidates as baselines. Human Performance: We pick each reference answer provided by an annotator, and compute the F1 with respect to the remaining references, as described in section 4.2.1. Each reference answer is treated as the prediction, and the remaining n-1 answers are treated as the gold reference. The average of the maximum F1 across all reference answers is computed as the human baseline. 
 Question: Were other baselines tested to compare with the neural baseline?
[EX A]: SVM No-Answer Baseline (NA)  Word Count Baseline Human Performance

[EX Q]: We first use state-of-the-art PDTB taggers for our baseline BIBREF13 , BIBREF12 for the evaluation of the causality prediction of our models ( BIBREF12 requires sentences extracted from the text as its input, so we used our parser to extract sentences from the message). 
 Question: What baselines did they consider?
[EX A]: state-of-the-art PDTB taggers

[EX Q]: Given a proof state $= (_, _)$ , where $_$ and $_$ denote a substitution set and a proof score, respectively, unification is computed as follows: The resulting proof score of $g$ is given by:

$$ \begin{aligned} \max _{f \in \mathcal {K}} & \; {unify}_(g, [f_{p}, f_{s}, f_{o}], (\emptyset , )) \\ & = \max _{f \in \mathcal {K}} \; \min \big \lbrace , \operatorname{k}(_{\scriptsize {grandpaOf}:}, _{f_{p}:}),\\ &\qquad \qquad \qquad \operatorname{k}(_{{abe}:}, _{f_{s}:}), \operatorname{k}(_{{bart}:}, _{f_{o}:}) \big \rbrace , \end{aligned}$$ (Eq. 3)

where $f \triangleq [f_{p}, f_{s}, f_{o}]$ is a fact in $\mathcal {K}$ denoting a relationship of type $f_{p}$ between $f_{s}$ and $f_{o}$ , $_{s:}$ is the embedding representation of a symbol $s$ , $$ denotes the initial proof score, and $\operatorname{k}({}\cdot {}, {}\cdot {})$ denotes the RBF kernel. 
 Question: How are proof scores calculated?
[EX A]:
'= ( , { ll k(h:, g:) if hV, gV

1 otherwise } )

where $_{h:}$ and $_{g:}$ denote the embedding representations of $h$ and $g$ , respectively.

