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.

Q: These are four conjunctively written Nguni languages (zul, xho, nbl, ssw), Afrikaans (afr) and English (eng), three disjunctively written Sotho languages (nso, sot, tsn), as well as tshiVenda (ven) and Xitsonga (tso). The Nguni languages are similar to each other and harder to distinguish. The same is true of the Sotho languages. Similar languages are to each other are:
- Nguni languages: zul, xho, nbl, ssw
- Sotho languages: nso, sot, tsn 
 Question: Which languages are similar to each other?

A: Nguni languages (zul, xho, nbl, ssw) Sotho languages (nso, sot, tsn)
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Q: This dataset was collected through Mechanical Turk using 100 simulated environments with a corresponding topological map and, to the best of our knowledge, it is the first of its kind for behavioral navigation.  We created a new dataset for the problem of following navigation instructions under the behavioral navigation framework of BIBREF5 . This dataset was created using Amazon Mechanical Turk and 100 maps of simulated indoor environments, each with 6 to 65 rooms. To the best of our knowledge, this is the first benchmark for comparing translation models in the context of behavioral robot navigation.

As shown in Table TABREF16 , the dataset consists of 8066 pairs of free-form natural language instructions and navigation plans for training. This training data was collected from 88 unique simulated environments, totaling 6064 distinct navigation plans (2002 plans have two different navigation instructions each; the rest has one). The dataset contains two test set variants:

While the dataset was collected with simulated environments, no structure was imposed on the navigation instructions while crowd-sourcing data. Thus, many instructions in our dataset are ambiguous. Moreover, the order of the behaviors in the instructions is not always the same. For instance, a person said “turn right and advance” to describe part of a route, while another person said “go straight after turning right” in a similar situation. The high variability present in the natural language descriptions of our dataset makes the problem of decoding instructions into behaviors not trivial. See Appendix A of the supplementary material for additional details on our data collection effort.

 
 Question: How were the navigation instructions collected?

A: using Amazon Mechanical Turk using simulated environments with topological maps
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Q: Despite this, all models in the literature rely on word-level representations, which keeps the models from being able to easily capture some of the lexical and morpho-syntactic cues known to denote irony, such as all caps, quotation marks and emoticons, and in Twitter, also emojis and hashtags. 
 Question: Which morphosyntactic features are thought to indicate irony or sarcasm?

A:
all caps quotation marks emoticons emojis hashtags
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