In this task, you will be presented with a context from an academic paper and a question separated with a 
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[EX Q]: Our experiments are run using the English$\rightarrow $Italian/German portions of the MuST-C corpus BIBREF25, which is extracted from TED talks, using the same train/validation/test split as provided with the corpus (see Table TABREF18). As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De). 
 Question: What dataset do they use for experiments?
[EX A]: English$\rightarrow $Italian/German portions of the MuST-C corpus As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De)

[EX Q]:  We defined a sequence labeling task to extract custom entities from user input. We assumed seven (7) possible entities (see Table TABREF43) to be recognized by the model: topic, subtopic, examination mode and level, question number, intent, as well as the entity other for remaining words in the utterance.   We defined a classification problem to predict the system's next action according to the given user input. We assumed 13 custom actions (see Table TABREF42) that we considered being our labels. In the conversational dataset, each input was automatically labeled by the rule-based system with the corresponding next action and the dialogue-id. Thus, no additional post-labeling was required.  
 Question: How does the IPA label data after interacting with users?
[EX A]: It defined a sequence labeling task to extract custom entities from user input and label the next action (out of 13  custom actions defined).

[EX Q]: Our prediction task is thus a straightforward binary classification task at the word level. We develop the following five groups of features to capture properties of how a word is used in the explanandum (see Table TABREF18 for the full list):

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Non-contextual properties of a word. These features are derived directly from the word and capture the general tendency of a word being echoed in explanations.

Word usage in an OP or PC (two groups). These features capture how a word is used in an OP or PC. As a result, for each feature, we have two values for the OP and PC respectively.

How a word connects an OP and PC. These features look at the difference between word usage in the OP and PC. We expect this group to be the most important in our task.

General OP/PC properties. These features capture the general properties of a conversation. They can be used to characterize the background distribution of echoing.

Table TABREF18 further shows the intuition for including each feature, and condensed $t$-test results after Bonferroni correction. Specifically, we test whether the words that were echoed in explanations have different feature values from those that were not echoed. In addition to considering all words, we also separately consider stopwords and content words in light of Figure FIGREF8. Here, we highlight a few observations:

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Although we expect more complicated words (#characters) to be echoed more often, this is not the case on average. We also observe an interesting example of Simpson's paradox in the results for Wordnet depth BIBREF38: shallower words are more likely to be echoed across all words, but deeper words are more likely to be echoed in content words and stopwords. 
 Question: What features are proposed?
[EX A]:
Non-contextual properties of a word Word usage in an OP or PC (two groups) How a word connects an OP and PC General OP/PC properties