instruction:
In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
question:
The overall Total Accuracy score reported in table TABREF19 using the entire feature set is 549. 
answer:
Do they experiment with the dataset?


question:
We compare FSDM with four baseline methods and two ablations.

NDM BIBREF7 proposes a modular end-to-end trainable network. It applies de-lexicalization on user utterances and responses.

LIDM BIBREF9 improves over NDM by employing a discrete latent variable to learn underlying dialogue acts. This allows the system to be refined by reinforcement learning.

KVRN BIBREF13 adopts a copy-augmented Seq2Seq model for agent response generation and uses an attention mechanism on the KB. It does not perform belief state tracking.

TSCP/RL BIBREF10 is a two-stage CopyNet which consists of one encoder and two copy-mechanism-augmented decoders for belief state and response generation.
answer:
What baselines have been used in this work?


question:
The SemEval-2010 benchmark dataset BIBREF0 is composed of 244 scientific articles collected from the ACM Digital Library (conference and workshop papers).
answer:

how many articles are in the dataset?