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.

Q: 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.

A: What is novel in author's approach?
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Q: We focus here on the Europarl domain, for which we have ample data in several languages, and use as in-domain training data the Europarl corpus BIBREF5 for two translation directions: English INLINEFORM0 German and English INLINEFORM1 French. When measuring out-of-domain performance, we will use the WMT newstest 2014. or French we use samples from News-Commentary-11 and Wikipedia from WMT 2014 shared translation task, as well as the Multi-UN BIBREF9 and EU-Bookshop BIBREF10 corpora. For German, we use samples from News-Commentary-11, Rapid, Common-Crawl (WMT 2017) and Multi-UN (see table TABREF5 ).

A: what dataset is used?
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Q: We call the model as a modified Toulmin's model. It contains five argument components, namely claim, premise, backing, rebuttal, and refutation. When annotating a document, any arbitrary token span can be labeled with an argument component; the components do not overlap.

A:
What argument components do the ML methods aim to identify?
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