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.

Input: Consider Input: It is a well known fact that NLP applications in industrial settings often have to deal with the noisy data. There are different kinds of possible noise namely non-canonical text such as spelling mistakes, typographic errors, colloquialisms, abbreviations, slang, internet jargon, emojis, embedded metadata (such as hashtags, URLs, mentions), non standard syntactic constructions and spelling variations, grammatically incorrect text, mixture of two or more languages to name a few. Such noisy data is a hallmark of user generated text content and commonly found on social media, chats, online reviews, web forums to name a few.
Question: What kind is noise is present in typical industrial data?

Output: non-canonical text such as spelling mistakes, typographic errors, colloquialisms, abbreviations, slang, internet jargon, emojis, embedded metadata (such as hashtags, URLs, mentions), non standard syntactic constructions and spelling variations, grammatically incorrect text, mixture of two or more languages

Input: Consider Input: The dataset includes about 6,000 triples, comprised of videos, questions, and answer spans manually collected from screencast tutorial videos with spoken narratives for a photo-editing software.
Question: What kind of instructional videos are in the dataset?

Output: tutorial videos for a photo-editing software

Input: Consider Input: All baselines are Transformer models in their base configuration BIBREF11, using 6 encoder and decoder layers, with model and hidden dimensions of 512 and 2048 respectively, and 8 heads for all attention layers. For initial multi-task experiments, all model parameters were shared BIBREF12, but performance was down by multiple BLEU points compared to the baselines.
Question: What baselines non-adaptive baselines are used?
Output: Transformer models in their base configuration BIBREF11, using 6 encoder and decoder layers, with model and hidden dimensions of 512 and 2048 respectively, and 8 heads for all attention layers