In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Q: We evaluate the TN topic model quantitatively with standard topic model measures such as test-set perplexity, likelihood convergence and clustering measures. Qualitatively, we evaluate the model by visualizing the topic summaries, authors' topic distributions and by performing an automatic labeling task.

A: What are the measures of "performance" used in this paper?
****
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):

[itemsep=0pt,leftmargin=*,topsep=0pt]

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:

[itemsep=0pt,leftmargin=*,topsep=0pt]

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.

A: What features are proposed?
****
Q: While the neural syntax-agnostic system obtains superior performance on the L1 data, the two syntax-based systems both produce better analyses on the L2 data. Furthermore, as illustrated in the comparison between different parsers, the better the parsing results we get, the better the performance on L2 we achieve. This shows that syntactic parsing is important in semantic construction for learner Chinese. The main reason, according to our analysis, is that the syntax-based system may generate correct syntactic analyses for partial grammatical fragments in L2 texts, which provides crucial information for SRL.

A:
Do the authors suggest why syntactic parsing is so important for semantic role labelling for interlanguages?
****