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.

Input: Consider Input: We propose to derive rewards from human-human dialogues by assigning positive values to contextualised responses seen in the data, and negative values to randomly chosen responses due to lacking coherence (also referred to as 'non-human-like responses') see example in Tables TABREF29 and TABREF30 .
Question: How do they obtain human generated policies?

Output: derive rewards from human-human dialogues by assigning positive values to contextualised responses seen in the data, and negative values to randomly chosen responses due to lacking coherence

Input: Consider Input: For the embeddings, we relied on $AraVec$ BIBREF30 for Arabic, FastText BIBREF31 for French, and Word2vec Google News BIBREF32 for English .
Question: What monolingual word representations are used?

Output: AraVec for Arabic, FastText for French, and Word2vec Google News for English.

Input: Consider Input: To create our training dataset, we followed an approach similar to LASER. The dataset contains 6 languages: English, Spanish, German, Dutch, Korean and Chinese Mandarin. The dataset was created by using translations provided by Tatoeba and OpenSubtitles BIBREF16.
Question: Which corpus do they use?
Output: The dataset was created by using translations provided by Tatoeba and OpenSubtitles BIBREF16.