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: In target data, the code is written in Python programming language. 
 Question: What programming language is target language?

SOLUTION: Python

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: In this paper we have formalized the problem of automatic fill-on-the-blanks quiz generation using two well-defined learning schemes: sequence classification and sequence labeling. 
 Question: Which two schemes are used?

SOLUTION:
sequence classification sequence labeling