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.

Input: Consider Input: We compare our model with two sets of baselines:

MemN2N BIBREF12 is an end-to-end trainable version of memory networks BIBREF9 . Attentive and Impatient Readers BIBREF6 use bidirectional LSTMs to encode question and evidence, and do classification over a large vocabulary based on these two encodings. 
 Question: What are the baselines?

Output: MemN2N BIBREF12 Attentive and Impatient Readers BIBREF6


Input: Consider Input: Several recent works have investigated jointly training the acoustic model with a masking speech enhancement model BIBREF11, BIBREF12, BIBREF13, but these works did not evaluate their system on speech enhancement metrics. Indeed, our internal experiments show that without access to the clean data, joint training severely harms performance on these metrics. 
 Question: Which frozen acoustic model do they use?

Output: a masking speech enhancement model BIBREF11, BIBREF12, BIBREF13


Input: Consider Input:  We draw our data from news publications, wine reviews, and Reddit, which in addition to large volume, also let us characterize binomials in new ways, and analyze differences in binomial orderings across communities and over time.  We develop new metrics for the agreement of binomial orderings across communities and the movement of binomial orderings over time. Using subreddits as communities, these metrics reveal variations in orderings, some of which suggest cultural change influencing language. We develop a null model to determine how much variation in binomial orderings we might expect across communities and across time, if binomial orderings were randomly ordered according to global asymmetry values.  
 Question: How is order of binomials tracked across time?


Output: draw our data from news publications, wine reviews, and Reddit develop new metrics for the agreement of binomial orderings across communities and the movement of binomial orderings over time  develop a null model to determine how much variation in binomial orderings we might expect across communities and across time