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]: 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?
[A]: 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

[Q]: FastTextBIBREF4: It uses bag of words and bag of n-grams as features for text classification, capturing partial information about the local word order efficiently. BiLSTM: Unlike feedforward neural networks, recurrent neural networks like BiLSTMs use memory based on history information to learn long-distance features and then predict the output. We use a two-layer BiLSTM architecture with GloVe word embeddings as a strong RNN baseline. BERT BIBREF5: It is a contextualized word representation model that uses bidirectional transformers, pretrained on a large $3.3B$ word corpus. We use the $BERT_{large}$ model finetuned on the training dataset.
Question: What is the baseline for the experiments?
[A]: FastText BiLSTM BERT

[Q]: Given that words from texts of the same class belong to the same context, it is possible to model word vectors of each class as word subspaces and efficiently compare them in terms of similarity by using canonical angles between the word subspaces.
Question: What can word subspace represent?
[A]:
Word vectors, usually in the context of others within the same class