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:  Recurrent Neural Network (RNN) Language Models
are trained to output the probability distribution of the upcoming word given a context, without explicitly representing the structure of the context BIBREF9, BIBREF10. ActionLSTM
models the linearized bracketed tree structure of a sentence by learning to predict the next action required to construct a phrase-structure parse BIBREF16. Generative Recurrent Neural Network Grammars (RNNG)
jointly model the word sequence as well as the underlying syntactic structure BIBREF18. 
 Question: What are the baseline models?

A: Recurrent Neural Network (RNN) ActionLSTM Generative Recurrent Neural Network Grammars (RNNG)
****
Q: We tested the system on two datasets, different in size and complexity of the addressed language.

Experimental Evaluation ::: Datasets ::: NLU-Benchmark dataset
The first (publicly available) dataset, NLU-Benchmark (NLU-BM), contains $25,716$ utterances annotated with targeted Scenario, Action, and involved Entities. Experimental Evaluation ::: Datasets ::: ROMULUS dataset
The second dataset, ROMULUS, is composed of $1,431$ sentences, for each of which dialogue acts, semantic frames, and corresponding frame elements are provided. This dataset is being developed for modelling user utterances to open-domain conversational systems for robotic platforms that are expected to handle different interaction situations/patterns – e.g., chit-chat, command interpretation. 
 Question: Which publicly available NLU dataset is used?

A: ROMULUS dataset NLU-Benchmark dataset
****
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
****
