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: The baseline is a hierarchical phrase-based system BIBREF29 with a 4-gram language model, with feature weights tuned using MIRA BIBREF30 . Therefore, an appropriate additional baseline would be to mark translation rules with these indicator functions but without the scores, akin to identifying rules with phrases in them (Baseline + SegOn). 
 Question: Which translation systems do they compare against?

Output: hierarchical phrase-based system BIBREF29 appropriate additional baseline would be to mark translation rules with these indicator functions but without the scores, akin to identifying rules with phrases in them (Baseline + SegOn)


Input: Consider Input: This step entails counting occurrences of all words in the training corpus and sorting them in order of decreasing occurrence. As mentioned, the vocabulary is taken to be the INLINEFORM0 most frequently occurring words, that occur at least some number INLINEFORM1 times. It is implemented in Spark as a straight-forward map-reduce job. 
 Question: Do they perform any morphological tokenization?

Output: No


Input: Consider Input: To study how the multimodal context can boost the performance compared to an unimodal context we evaluate different models: a Feature Concatenation Model (FCM), a Spatial Concatenation Model (SCM) and a Textual Kernels Model (TKM) 
 Question: What models do they propose?


Output: Feature Concatenation Model (FCM) Spatial Concatenation Model (SCM) Textual Kernels Model (TKM)