instruction:
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.
question:
As a framework, we use the sentence classifier configuration of FLAIR BIBREF46 with a biLSTM encoder/classifier architecture fed by character and word level representations composed of a concatenation of fixed 300 dimensional GloVe embeddings BIBREF47, pre-trained contextualized FLAIR word embeddings, and pre-trained contextualized character embeddings from AllenNLP BIBREF48 with FLAIR's default hyperparameters.
Question: Are some models evaluated using this metric, what are the findings?
answer:
Yes

question:
In Figure FIGREF15 , we plot the zero-resource German and Japanese test set accuracy as a function of the number of steps taken, with and without adversarial training. The plot shows that the variation in the test accuracy is reduced with adversarial training, which suggests that the cross-lingual performance is more consistent when adversarial training is applied.
Question: Do any of the evaluations show that adversarial learning improves performance in at least two different language families?
answer:
Yes

question:
In order to explore the problem space, we thus specifically target five binary classification problems addressed in BIBREF17 , BIBREF18 , i.e presence/absence of consonants, phonemic nasal, bilabial, high-front vowels and high-back vowels.
Question: What are the five different binary classification tasks?
answer:
presence/absence of consonants, phonemic nasal, bilabial, high-front vowels and high-back vowels.