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: Without ELMo (the same setting as 4-th row in Table 3 ), our data settings is the same as qin-EtAl:2017:Long whose performance was state-of-the-art and will be compared directly. We see that even without the pre-trained ELMo encoder, our performance is better, which is mostly attributed to our better sentence pair representations.

A: Why does their model do better than prior models?
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Q: Conditional Random Fields
Conditional Random Fields (CRF) BIBREF10 are a standard approach when dealing with sequential data in the context of sequence labeling. BiLSTM-CRF
Prior to the emergence of deep neural language models, BiLSTM-CRF models BIBREF11 had achieved state-of-the-art results for the task of sequence labeling. Multi-Task Learning
Multi-Task Learning (MTL) BIBREF15 has become popular with the progress in deep learning. BioBERT
Deep neural language models have recently evolved to a successful method for representing text. In particular, Bidirectional Encoder Representations from Transformers (BERT) outperformed previous state-of-the-art methods by a large margin on various NLP tasks BIBREF17.

A: What baseline systems are proposed?
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Q: As before, the training and test sets contain some 30,000 and 5,000 sentence pairs, respectively

A:
How many samples did they generate for the artificial language?
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