instruction:
In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
question:
It is worth mentioning that the collected texts contain a large quantity of errors of several types: orthographic, syntactic, code-switched words (i.e. words not in the required language), jokes, etc. Hence, the original written sentences have been processed in order to produce "cleaner" versions, in order to make the data usable for some research purposes (e.g. to train language models, to extract features for proficiency assessment, ...).
answer:
Are any of the utterances ungrammatical?

question:
In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. 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).
answer:
How does their model learn using mostly raw data?

question:
Following BIBREF3, we hypothesize that a sentence representation in mBERT is composed of a language-specific component, which identifies the language of the sentence, and a language-neutral component, which captures the meaning of the sentence in a language-independent way. We assume that the language-specific component is similar across all sentences in the language.

We thus try to remove the language-specific information from the representations by centering the representations of sentences in each language so that their average lies at the origin of the vector space. We then analyze the semantic properties of both the original and the centered representations using a range of probing tasks.
answer:
How they show that mBERT representations can be split into a language-specific component and a language-neutral component?