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.

On the same architecture, our RCRN outperforms ablative baselines BiLSTM by INLINEFORM2 and 3L-BiLSTM by INLINEFORM3 on average across 16 datasets.
By how much do they outperform BiLSTMs in Sentiment Analysis?

Finally, a comparison was made between the Skip-gram model W10N20 obtained at the 50th epoch and the other two W2V in Italian present in the literature (BIBREF9 and BIBREF10). The first test (Table TABREF15) was performed considering all the analogies present, and therefore evaluating as an error any analogy that was not executable (as it related to one or more words absent from the vocabulary).

As it can be seen, regardless of the metric used, our model has significantly better results than the other two models, both overall and within the two macro-areas.
Are the word embeddings evaluated?

Similar to BIBREF9, we experiment with SVM with RBF kernel, with features that represent (1) the simple characteristics of the argument tree and (2) the linguistic characteristics of the claim.

What models that rely only on claim-specific linguistic features are used as baselines?