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 evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.
Question: Are results reported only for English data?
Example output: No
Example explanation: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.
Q: 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?
A:
supply an NMT system with knowledge regarding the speaker and interlocutor of first-person sentences