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: To collect a diverse training dataset, we have randomly sampled 1000 posts each from the subreddits politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage.
Question: what are the topics pulled from Reddit?

SOLUTION: politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage.

PROBLEM: To be able to employ the trained model, test sets are first translated to English via machine translation and then inference takes place. For machine translation, Google translation API is used.
Question: how did the authors translate the reviews to other languages?

SOLUTION: Using Google translation API.

PROBLEM: We found that the unsupervised morphological analyzer capable of using lattices improved the accuracy of phoneme recognition and word segmentation. Consequently, this result suggests that this word segmentation method considers the multiple hypothesis of speech recognition as a whole and reduces uncertainty such as variability in recognition by using the syllable recognition results in the lattice format.
Question: How do they show that acquiring names of places helps self-localization?

SOLUTION:
unsupervised morphological analyzer capable of using lattices improved the accuracy of phoneme recognition and word segmentation Consequently, this result suggests that this word segmentation method considers the multiple hypothesis of speech recognition as a whole and reduces uncertainty such as variability in recognition