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.

Example Input: 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 source of the CAIS dataset?
Example Output: the $\mathbf {C}$hinese $\mathbf {A}$rtificial $\mathbf {I}$ntelligence $\mathbf {S}$peakers (CAIS)

Example Input: Because of this, we do not claim that this dataset can be considered a ground truth.
Question: How is the ground truth for fake news established?
Example Output: Ground truth is not established in the paper

Example Input: Our goal is to supply an NMT system with knowledge regarding the speaker and interlocutor of first-person sentences, in order to produce the desired target-side morphology when the information is not available in the source sentence. The approach we take in the current work is that of black-box injection, in which we attempt to inject knowledge to the input in order to influence the output of a trained NMT system, without having access to its internals or its training procedure as proposed by vanmassenhove-hardmeier-way:2018:EMNLP. To verify this, we experiment with translating the sentences with the following variations: No PrefixThe baseline translation as returned by the GMT system. "He said:"Signaling a male speaker. We expect to further skew the system towards masculine forms. "She said:"Signaling a female speaker and unknown audience. As this matches the actual speaker's gender, we expect an improvement in translation of first-person pronouns and verbs with first-person pronouns as subjects. "I said to them:"Signaling an unknown speaker and plural audience. "He said to them:"Masculine speaker and plural audience. "She said to them:"Female speaker and plural audiencethe complete, correct condition. We expect the best translation accuracy on this setup. "He/she said to him/her"Here we set an (incorrect) singular gender-marked audience, to investigate our ability to control the audience morphology.
Question: What are the components of the black-box context injection system?
Example Output:
supply an NMT system with knowledge regarding the speaker and interlocutor of first-person sentences