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.

To account for sequences of words and characters that might carry useful information, we extracted word unigrams, bigrams, and trigrams as features.
Question: Which features do they use to model Twitter messages?
word unigrams, bigrams, and trigrams

We collect utterances from the $\mathbf {C}$hinese $\mathbf {A}$rtificial $\mathbf {I}$ntelligence $\mathbf {S}$peakers (CAIS), and annotate them with slot tags and intent labels. The training, validation and test sets are split by the distribution of intents, where detailed statistics are provided in the supplementary material. Since the utterances are collected from speaker systems in the real world, intent labels are partial to the PlayMusic option. We adopt the BIOES tagging scheme for slots instead of the BIO2 used in the ATIS, since previous studies have highlighted meaningful improvements with this scheme BIBREF30 in the sequence labeling field.
Question: What is the domain of their collected corpus?
speaker systems in the real world

In cooperation with the ruling regime, Weibo sets strict control over the content published under its service BIBREF0. According to Zhu et al. zhu-etal:2013, Weibo uses a variety of strategies to target censorable posts, ranging from keyword list filtering to individual user monitoring. Among all posts that are eventually censored, nearly 30% of them are censored within 530 minutes, and nearly 90% within 24 hours BIBREF1. We hypothesize that the former are done automatically, while the latter are removed by human censors.
Question: Is is known whether Sina Weibo posts are censored by humans or some automatic classifier?
No