TASK DEFINITION: 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.
PROBLEM: We apply the Generative Pre-trained Transformer (GPT) BIBREF2 as our pre-trained language model.
Question: What pretrained LM is used?

SOLUTION: Generative Pre-trained Transformer (GPT)

PROBLEM: The main set of experiments uses a Linear Support Vector Machine (Joachims, 1998) to classify dialects using CxG features. This paper has used data-driven language mapping to select national dialects of English to be included in a global dialect identification model.
Question: What do the models that they compare predict?

SOLUTION: national dialects of English

PROBLEM: For supertagging, we observe that the baseline cross entropy trained model improves its predictions with beam search decoding compared to greedy decoding by 2 accuracy points, which suggests that beam search is already helpful for this task, even without search-aware training.
Question: By how much do they outperform basic greedy and cross-entropy beam decoding?

SOLUTION:
2 accuracy points