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: Our novelties include:

Using self-play learning for the neural response ranker (described in detail below).

Optimizing neural models for specific metrics (e.g. diversity, coherence) in our ensemble setup.

Training a separate dialog model for each user, personalizing our socialbot and making it more consistent.

Using a response classification predictor and a response classifier to predict and control aspects of responses such as sentiment, topic, offensiveness, diversity etc.

Using a model predictor to predict the best responding model, before the response candidates are generated, reducing computational expenses.

Using our entropy-based filtering technique to filter all dialog datasets, obtaining higher quality training data BIBREF3.

Building big, pre-trained, hierarchical BERT and GPT dialog models BIBREF6, BIBREF7, BIBREF8.

Constantly monitoring the user input through our automatic metrics, ensuring that the user stays engaged. 
 Question: What is novel in author's approach?

SOLUTION: They use self-play learning , optimize the model for specific metrics, train separate models per user, use model  and response classification predictors, and filter the dataset to obtain higher quality training data.

PROBLEM: Experiments on two publicly available datasets (Camrest BIBREF11 and InCar Assistant BIBREF6) confirm the effectiveness of the KB-retriever. 
 Question: Which dialog datasets did they experiment with?

SOLUTION: Camrest InCar Assistant

PROBLEM: One of the several formats into which FHIR can be serialized is RDF. However, because RDF was designed as an abstract information model and FHIR was designed for operational use in a healthcare setting, there is the potential for a slight mismatch between the models. This comes up in two ways: One, RDF makes statements of fact, whereas FHIR makes records of events. Two, RDF is intended to have the property of monotonicity, meaning that previous facts cannot be invalidated by new facts. 
 Question: How are FHIR and RDF combined?

SOLUTION:
RDF was designed as an abstract information model and FHIR was designed for operational use in a healthcare setting RDF makes statements of fact, whereas FHIR makes records of events RDF is intended to have the property of monotonicity, meaning that previous facts cannot be invalidated by new facts