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.

[EX Q]: As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., "to be glad" is positive). Suppose that events $x_1$ are $x_2$ are in the discourse relation of Cause (i.e., $x_1$ causes $x_2$). If the seed lexicon suggests $x_2$ is positive, $x_1$ is also likely to be positive because it triggers the positive emotion. The fact that $x_2$ is known to be negative indicates the negative polarity of $x_1$. Similarly, if $x_1$ and $x_2$ are in the discourse relation of Concession (i.e., $x_2$ in spite of $x_1$), the reverse of $x_2$'s polarity can be propagated to $x_1$. Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network models that predict the polarity of a given event.
Question: How are relations used to propagate polarity?
[EX A]: based on the relation between events, the suggested polarity of one event can determine the possible polarity of the other event

[EX Q]: Using a set of semantically oriented tasks that require explicit semantic cross-lingual representations, we showed that mBERT contextual embeddings do not represent similar semantic phenomena similarly and therefore they are not directly usable for zero-shot cross-lingual tasks.
Question: What challenges this work presents that must be solved to build better language-neutral representations?
[EX A]: contextual embeddings do not represent similar semantic phenomena similarly and therefore they are not directly usable for zero-shot cross-lingual tasks

[EX Q]: Logistic regression: To produce the representation of the input, we concatenate the Bag-Of-Words representation of the document with the Bag-Of-Words representation of the question. LSTM: We start with a concatenation of the sequence of indexes of the document with the sequence of indexes of the question. Them we feed an LSTM network with this vector and use the final state as the representation of the input. Finally, we apply a logistic regression over this representation to produce the final decision. End-to-end memory networks: This architecture is based on two different memory cells (input and output) that contain a representation of the document. Deep projective reader: This is a model of our own design, largely inspired by the efficient R-net reader BIBREF12 .
Question: What baselines are presented?
[EX A]:
Logistic regression LSTM End-to-end memory networks Deep projective reader