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 evaluate the generations with the ROUGE BIBREF29 and METEOR BIBREF30 metrics using the true sentences as targets.
Question: What metrics are used for evaluation?

SOLUTION: ROUGE BIBREF29 and METEOR BIBREF30

PROBLEM: In our experiments, we used TF.IDF-based features over the title and over the content of the article we wanted to classify. We had these features twice once for the title and once for the the content of the article, as we wanted to have two different representations of the same article. Thus, we used a total of 1,100 TF.IDF-weighted features (800 content + 300 title), limiting the vocabulary to the top 800 and 300 words, respectively (which occurred in more than five articles).
Question: what lexical features did they experiment with?

SOLUTION: TF.IDF-based features

PROBLEM: In this paper, we propose to represent each word with an expressive multimodal distribution, for multiple distinct meanings, entailment, heavy tailed uncertainty, and enhanced interpretability. For example, one mode of the word 'bank' could overlap with distributions for words such as 'finance' and 'money', and another mode could overlap with the distributions for 'river' and 'creek'. It is our contention that such flexibility is critical for both qualitatively learning about the meanings of words, and for optimal performance on many predictive tasks.
Question: How does this compare to contextual embedding methods?

SOLUTION:
represent each word with an expressive multimodal distribution, for multiple distinct meanings, entailment, heavy tailed uncertainty, and enhanced interpretability. For example, one mode of the word 'bank' could overlap with distributions for words such as 'finance' and 'money', and another mode could overlap with the distributions for 'river' and 'creek'.