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.

[Q]: To simplify the task, we set up a binary classification: candidates who have been liked or shortlisted are considered part of the hirable class and others part of the not hirable class. 
 Question: How is "hirability" defined?
[A]: candidates who have been liked or shortlisted are considered part of the hirable class


[Q]: Tests and Gold-Standard Data for Intrinsic Evaluation
Using the gold standard data (described below), we performed three types of tests:

Class Membership Tests: embeddings corresponding two member of the same semantic class (e.g. “Months of the Year", “Portuguese Cities", “Smileys") should be close, since they are supposed to be found in mostly the same contexts.

Class Distinction Test: this is the reciprocal of the previous Class Membership test. Embeddings of elements of different classes should be different, since words of different classes ere expected to be found in significantly different contexts.

Word Equivalence Test: embeddings corresponding to synonyms, antonyms, abbreviations (e.g. “porque" abbreviated by “pq") and partial references (e.g. “slb and benfica") should be almost equal, since both alternatives are supposed to be used be interchangeable in all contexts (either maintaining or inverting the meaning).

Therefore, in our tests, two words are considered:

distinct if the cosine of the corresponding embeddings is lower than 0.70 (or 0.80).

to belong to the same class if the cosine of their embeddings is higher than 0.70 (or 0.80).

equivalent if the cosine of the embeddings is higher that 0.85 (or 0.95). 
 Question: What intrinsic evaluation metrics are used?
[A]: Class Membership Tests Class Distinction Test Word Equivalence Test


[Q]: We will compare to the more recent cross-lingual language model XLM BIBREF12, as well as the state-of-the-art CoNLL 2018 shared task results with predicted tokenisation and segmentation in an updated version of the paper. In French, no extensive work has been done due to the limited availability of NER corpora. We compare our model with the strong baselines settled by BIBREF49, who trained both CRF and BiLSTM-CRF architectures on the FTB and enhanced them using heuristics and pretrained word embeddings. In the TRANSLATE-TRAIN setting, we report the best scores from previous literature along with ours. BiLSTM-max is the best model in the original XNLI paper, mBERT which has been reported in French in BIBREF52 and XLM (MLM+TLM) is the best-presented model from BIBREF50. 
 Question: What is the state of the art?
[A]:
POS and DP task: CONLL 2018
NER task: (no extensive work) Strong baselines CRF and BiLSTM-CRF
NLI task: mBERT or XLM (not clear from text)


