TASK DEFINITION: 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.
PROBLEM: Machine-machine Interaction A related line of work explores simulation-based dialogue generation, where the user and system roles are simulated to generate a complete conversation flow, which can then be converted to natural language using crowd workers BIBREF1. Such a framework may be cost-effective and error-resistant since the underlying crowd worker task is simpler, and semantic annotations are obtained automatically. It is often argued that simulation-based data collection does not yield natural dialogues or sufficient coverage, when compared to other approaches such as Wizard-of-Oz. We argue that simulation-based collection is a better alternative for collecting datasets like this owing to the factors below. 
 Question: How did they gather the data?

SOLUTION: simulation-based dialogue generation, where the user and system roles are simulated to generate a complete conversation flow, which can then be converted to natural language using crowd workers 

PROBLEM: 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?

SOLUTION: No

PROBLEM: Modules: Modules are the basic building blocks of different models. In LeafNATS, we provide ready-to-use modules for constructing recurrent neural network (RNN)-based sequence-to-sequence (Seq2Seq) models for NATS, e.g., pointer-generator network BIBREF1 . These modules include embedder, RNN encoder, attention BIBREF24 , temporal attention BIBREF6 , attention on decoder BIBREF2 and others. We also use these basic modules to assemble a pointer-generator decoder module and the corresponding beam search algorithms. The embedder can also be used to realize the embedding-weights sharing mechanism BIBREF2 . 
 Question: What models are included in the toolkit?

SOLUTION:
 recurrent neural network (RNN)-based sequence-to-sequence (Seq2Seq) models for NATS