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.
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Question: 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.

Answer: What is novel in author's approach?


Question: $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$ are the best systems with the highest recall and F1-score respectively.

Answer: What was previous state-of-the-art approach?


Question: To conclude this section, our model reliable corrects grammatical, spelling and word order errors on , with more mixed performance on lexical choice errors and some unnecessary paraphrasing of the input.

Answer:
What error types is their model more reliable for?