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.

[EX 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?
[EX A]: using Amazon Mechanical Turk using simulated environments with topological maps

[EX Q]: This kind of error affects the precision and recall for the “troll” and “engage” classes. A solution to this problem may be the inclusion of longer parts of the conversation.  
 Question: What potential solutions are suggested?
[EX A]:  inclusion of longer parts of the conversation

[EX Q]: In order to train a neural g2p system, one needs a large quantity of pronunciation data. A standard dataset for g2p is the Carnegie Mellon Pronouncing Dictionary BIBREF12 . However, that is a monolingual English resource, so it is unsuitable for our multilingual task. Instead, we use the multilingual pronunciation corpus collected by deri2016grapheme for all experiments. This corpus consists of spelling–pronunciation pairs extracted from Wiktionary. It is already partitioned into training and test sets. Corpus statistics are presented in Table TABREF10 In addition to the raw IPA transcriptions extracted from Wiktionary, the corpus provides an automatically cleaned version of transcriptions. Cleaning is a necessary step because web-scraped data is often noisy and may be transcribed at an inconsistent level of detail. The data cleaning used here attempts to make the transcriptions consistent with the phonemic inventories used in Phoible BIBREF4 .  
 Question: what datasets did they use?
[EX A]:
the Carnegie Mellon Pronouncing Dictionary BIBREF12 the multilingual pronunciation corpus collected by deri2016grapheme  ranscriptions extracted from Wiktionary