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.

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.
Question: What is novel in author's approach?

A: 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.
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Q: This step consists of generating a query out of the claim and querying a search engine (here, we experiment with Google and Bing) in order to retrieve supporting documents. Rather than querying the search engine with the full claim (as on average, a claim is two sentences long), we generate a shorter query following the lessons highlighted in BIBREF0 . We rank the words by means of tf-idf. We compute the idf values on a 2015 Wikipedia dump and the English Gigaword. BIBREF0 suggested that a good way to perform high-quality search is to only consider the verbs, the nouns and the adjectives in the claim; thus, we exclude all words in the claim that belong to other parts of speech. Moreover, claims often contain named entities (e.g., names of persons, locations, and organizations); hence, we augment the initial query with all the named entities from the claim's text. We use IBM's AlchemyAPI to identify named entities. Ultimately, we generate queries of 510 tokens, which we execute against a search engine. We then collect the snippets and the URLs in the results, skipping any result that points to a domain that is considered unreliable.
Question: How are the potentially relevant text fragments identified?

A: Generate a query out of the claim and querying a search engine, rank the words by means of TF-IDF, use IBM's AlchemyAPI to identify named entities, generate queries of 510 tokens, which execute against a search engine, and collect the snippets and the URLs in the results, skipping any result that points to a domain that is considered unreliable.
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Q: Fortunately, 180221 of 231162 author names could be matched successfully. There are many reasons for the remaining uncovered cases. 9073 Latin names could not be found in the name dictionary ENAMDICT and 14827 name matchings between the names' Latin and kanji representations did not succeed. These names might be missing at all in the dictionary, delivered in a very unusual format that the tool does not cover, or might not be Japanese or human names at all. Of course, Japanese computer scientists sometimes also cooperate with foreign colleagues but our tool expects Japanese names and is optimized for them.
Question: How successful are they at matching names of authors in Japanese and English?

A:
180221 of 231162 author names could be matched successfully
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