instruction:
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.
question:
Detection: In hypernymy detection, the task is to classify whether pairs of words are in a hypernymy relation. For this task, we evaluate all models on five benchmark datasets: First, we employ the noun-noun subset of bless, which contains hypernymy annotations for 200 concrete, mostly unambiguous nouns. Second, we evaluate on leds BIBREF13 , which consists of 2,770 noun pairs balanced between positive hypernymy examples, and randomly shuffled negative pairs. Direction: In direction prediction, the task is to identify which term is broader in a given pair of words. For this task, we evaluate all models on three datasets described by BIBREF16 : On bless, the task is to predict the direction for all 1337 positive pairs in the dataset. Pairs are only counted correct if the hypernymy direction scores higher than the reverse direction, i.e. INLINEFORM0 . We reserve 10% of the data for validation, and test on the remaining 90%. On wbless, we follow prior work BIBREF17 , BIBREF18 and perform 1000 random iterations in which 2% of the data is used as a validation set to learn a classification threshold, and test on the remainder of the data. We report average accuracy across all iterations. Finally, we evaluate on bibless BIBREF16 , a variant of wbless with hypernymy and hyponymy pairs explicitly annotated for their direction. Graded Entailment: In graded entailment, the task is to quantify the degree to which a hypernymy relation holds. For this task, we follow prior work BIBREF19 , BIBREF18 and use the noun part of hyperlex BIBREF20 , consisting of 2,163 noun pairs which are annotated to what degree INLINEFORM0 is-a INLINEFORM1 holds on a scale of INLINEFORM2 . 
 Question: Which benchmark datasets are used?
answer:
noun-noun subset of bless leds BIBREF13 bless wbless bibless hyperlex BIBREF20


question:
If we take the best psr ensemble trained on CBT as a baseline, improving the model architecture as in BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , continuing to use the original CBT training data, lead to improvements of INLINEFORM0 and INLINEFORM1 absolute on named entities and common nouns respectively. By contrast, inflating the training dataset provided a boost of INLINEFORM2 while using the same model. 
 Question: How large are the improvements of the Attention-Sum Reader model when using the BookTest dataset?
answer:
INLINEFORM2 


question:
We compare Blse (Sections UID23 – UID30 ) to VecMap, Muse, and Barista (Section "Previous Work" ) as baselines, which have similar data requirements and to machine translation (MT) and monolingual (Mono) upper bounds which request more resources. 
 Question: what baseline do they compare to?
answer:

VecMap Muse Barista