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.

This step entails counting occurrences of all words in the training corpus and sorting them in order of decreasing occurrence. As mentioned, the vocabulary is taken to be the INLINEFORM0 most frequently occurring words, that occur at least some number INLINEFORM1 times. It is implemented in Spark as a straight-forward map-reduce job.
Question: Do they perform any morphological tokenization?
No

Figure FIGREF9 illustrates the overall architecture of the proposed Attentional Encoder Network (AEN), which mainly consists of an embedding layer, an attentional encoder layer, a target-specific attention layer, and an output layer. Embedding layer has two types: GloVe embedding and BERT embedding. Accordingly, the models are named AEN-GloVe and AEN-BERT.
Question: How is their model different from BERT?
overall architecture of the proposed Attentional Encoder Network (AEN), which mainly consists of an embedding layer, an attentional encoder layer, a target-specific attention layer, and an output layer.

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.
Question: Do the authors suggest why syntactic parsing is so important for semantic role labelling for interlanguages?
syntax-based system may generate correct syntactic analyses for partial grammatical fragments