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: When testing across languages, we report accuracy for two setups: average accuracy over each single-language model (Avg), and accuracy obtained when training on the concatenation of all languages but the target one (All). The latter setting is also used for the embeddings model. We report accuracy for all experiments.

A: What are the evaluation metrics used?
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Q: We evaluate our model on two public datasets, namely, Penn Treebank (PTB) BIBREF9 and the end-to-end (E2E) text generation corpus BIBREF10, which have been used in a number of previous works for text generation BIBREF0, BIBREF5, BIBREF11, BIBREF12. PTB consists of more than 40,000 sentences from Wall Street Journal articles whereas the E2E dataset contains over 50,000 sentences of restaurant reviews. The statistics of these two datasets are summarised in Table TABREF11.

A: Which dataset do they use for text modelling?
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Q: We extract 5 Surface and Lexical features, namely sequence length in number of tokens, average word length, type-token ratio, and lexical to tokens ratio (ratio of adjectives, verbs, nouns, and adverbs to tokens). We extracted two features that use a learned representation: Firstly, we get a sentence embedding feature that is built by averaging the word embeddings of an input sentence. Secondly, we extract a fastText representation using the fastText library with the same parameters as reported in Joulin et al. joulin2016bag.

A:
Do they differentiate insights where they are dealing with learned or engineered representations?
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