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]: Our model contains five independent decoders, one for each image in the sequence. This allows each decoder to learn a specific language model for each position of the sequence. 
 Question: Do the decoder LSTMs all have the same weights?
[A]: No


[Q]: We decided to use sentences involving at least one race- or gender-associated word. The sentences were intended to be short and grammatically simple. We also wanted some sentences to include expressions of sentiment and emotion, since the goal is to test sentiment and emotion systems. 
 Question: What criteria are used to select the 8,640 English sentences?
[A]: Sentences involving at least one race- or gender-associated word,  sentence  have to be short and grammatically simple,  sentence have to  include expressions of sentiment and emotion.


[Q]: We use four systems to evaluate the difficulty of this dataset.  The first two are an information retrieval system and a word-association method, following the designs of BIBREF26 Clark2016CombiningRS. These are naive baselines that do not parse the question, but nevertheless may find some signal in a large corpus of text that helps guess the correct answer. The third is a CCG-style rule-based semantic parser written specifically for friction questions (the QuaRel INLINEFORM0 subset), but prior to data being collected. The last is a state-of-the-art neural semantic parser. We briefly describe each in turn. 
 Question: Which off-the-shelf tools do they use on QuaRel?
[A]:
information retrieval system word-association method  CCG-style rule-based semantic parser written specifically for friction questions state-of-the-art neural semantic parser