In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

[Q]: Motivated by this, we introduce resolution mode variables $\Pi = \lbrace \pi _1, \ldots , \pi _n\rbrace $ , where for each mention $j$ the variable $\pi _j \in \lbrace str, prec, attr\rbrace $ indicates in which mode the mention should be resolved. In our model, we define three resolution modes — string-matching (str), precise-construct (prec), and attribute-matching (attr) — and $\Pi $ is deterministic when $D$ is given (i.e. $P(\Pi |D)$ is a point distribution). We determine $\pi _j$ for each mention $m_j$ in the following way:

$\pi _j = str$ , if there exists a mention $m_i, i < j$ such that the two mentions satisfy the String Match sieve, the Relaxed String Match sieve, or the Strict Head Match A sieve in the Stanford multi-sieve system BIBREF1 .

$\pi _j = prec$ , if there exists a mention $m_i, i < j$ such that the two mentions satisfy the Speaker Identification sieve, or the Precise Constructs sieve.

$\pi _j = attr$ , if there is no mention $m_i, i < j$ satisfies the above two conditions. 
 Question: Are resolution mode variables hand crafted?
[A]: Yes-no


[Q]: We found interesting patterns which are learned by our model and help understand these monolingual gains. For example, a recurring pattern is that words in English which are translated to the same word, or to semantically close words, in the target language end up closer together after our transformation. For example, in the case of English-Spanish the following pairs were among the pairs whose similarity increased the most by applying our transformation: cellphone-telephone, movie-film, book-manuscript or rhythm-cadence, which are either translated to the same word in Spanish (i.e., teléfono and película in the first two cases) or are already very close in the Spanish space. More generally, we found that word pairs which move together the most tend to be semantically very similar and belong to the same domain, e.g., car-bicycle, opera-cinema, or snow-ice.

 
 Question: Why does the model improve in monolingual spaces as well? 
[A]: Abstractive


[Q]: Since pre-trained models operate on subword level, we need to estimate subword translation probabilities. For the purpose of evaluating the contextual representations learned by our model, we do not use part-of-speech tags. Contextualized representations are directly fed into Deep-Biaffine layers to predict arc and label scores. Table TABREF34 presents the Labeled Attachment Scores (LAS) for zero-shot dependency parsing. 
 Question: What metrics are used for evaluation?
[A]:
Extractive


