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]: As BIBREF0 discussed, the text-only UMT is fundamentally an ill-posed problem, since there are potentially many ways to associate target with source sentences. Intuitively, since the visual content and language are closely related, the image can play the role of a pivot "language" to bridge the two languages without paralleled corpus, making the problem "more well-defined" by reducing the problem to supervised learning.
Question: Why is this work different from text-only UNMT?
[A]: the image can play the role of a pivot "language" to bridge the two languages without paralleled corpus

[Q]: According to the categories mentioned in BIBREF5 , irony can be classified into 3 classes: verbal irony by means of a polarity contrast, the sentences containing expression whose polarity is inverted between the intended and the literal evaluation; other types of verbal irony, the sentences that show no polarity contrast between the literal and intended meaning but are still ironic; and situational irony, the sentences that describe situations that fail to meet some expectations. As ironies in the latter two categories are obscure and hard to understand, we decide to only focus on ironies in the first category in this work. Because of the lack of previous work and baselines on irony generation, we implement our model based on style transfer.
Question: What are the difficulties in modelling the ironic pattern?
[A]:
obscure and hard to understand lack of previous work and baselines on irony generation