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 Input:
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?

Ex Output:
noun-noun subset of bless leds BIBREF13 bless wbless bibless hyperlex BIBREF20

Ex Input:
We evaluate the TN topic model quantitatively with standard topic model measures such as test-set perplexity, likelihood convergence and clustering measures. Qualitatively, we evaluate the model by visualizing the topic summaries, authors' topic distributions and by performing an automatic labeling task.
Question: What are the measures of "performance" used in this paper?

Ex Output:
test-set perplexity, likelihood convergence and clustering measures visualizing the topic summaries, authors' topic distributions and by performing an automatic labeling task

Ex Input:
Language Model (LM): We train a two layer recurrent neural language model with GRU cells of hidden size 512.

Sequence-to-Sequence Attention Model (S2S): We train a two layer neural sequence to sequence model equipped with bi-linear attention function with GRU cells of hidden size 512. Linear Dynamical System (LDS): We also train a linear dynamical system as discussed in Section SECREF1 as one of our baselines for fair comparisons. Semi-Supervised SLDS (SLDS-X%): To gauge the usability of semi-supervision, we also train semi-supervised SLDS models with varying amount of labelled sentiment tags unlike the original model which uses 100% tagged data.
Question: What baselines are used?

Ex Output:
a two layer recurrent neural language model with GRU cells of hidden size 512 a two layer neural sequence to sequence model equipped with bi-linear attention function with GRU cells of hidden size 512 a linear dynamical system semi-supervised SLDS models with varying amount of labelled sentiment tags