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.

[EX Q]: To fine-tune BERT, we used a proprietary corpus that consists of hundreds of thousands of legal agreements.
[EX A]: How big is dataset used for fine-tuning BERT?

[EX Q]: We compare FSDM with four baseline methods and two ablations.

NDM BIBREF7 proposes a modular end-to-end trainable network. It applies de-lexicalization on user utterances and responses.

LIDM BIBREF9 improves over NDM by employing a discrete latent variable to learn underlying dialogue acts. This allows the system to be refined by reinforcement learning.

KVRN BIBREF13 adopts a copy-augmented Seq2Seq model for agent response generation and uses an attention mechanism on the KB. It does not perform belief state tracking.

TSCP/RL BIBREF10 is a two-stage CopyNet which consists of one encoder and two copy-mechanism-augmented decoders for belief state and response generation.
[EX A]: What baselines have been used in this work?

[EX Q]: The third person plural pronoun 'they' has no gender in English and most other languages (unlike the third person singular pronouns 'he', 'she', and 'it'). If these sentences are translated into French, then 'they' in the first sentence should be translated 'elles', as referring to Jane and Susan, and 'they' in the second sentence should be translated 'ils', as referring to Fred and George. A couple of examples: The word 'sie' in German serves as both the formal second person prounoun (always capitalized), the third person feminine singular, and the third person plural.
[EX A]:
What language do they explore?