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 discover topics from the collected tweets, we used a topic modeling approach that fuzzy clusters the semantically related words such as assigning "diabetes", "cancer", and "influenza" into a topic that has an overall "disease" theme BIBREF44 , BIBREF45 . Among topic models, Latent Dirichlet Allocation (LDA) BIBREF49 is the most popular effective model BIBREF50 , BIBREF19 as studies have shown that LDA is an effective computational linguistics model for discovering topics in a corpus BIBREF51 , BIBREF52 . We used the Mallet implementation of LDA BIBREF49 , BIBREF56 with its default settings to explore opinions in the tweets.
[EX A]: How were topics of interest about DDEO identified?

[EX Q]: First, many news articles begin with reporter names, media agencies, dates or other contents irrelevant to the content, e.g. "New York (CNN) ", "Jones Smith, May 10th, 2018:". We therefore apply simple regular expressions to remove these prefixes.

Second, to ensure that the summary is concise and the article contains enough salient information, we only keep articles with 10-150 words in the top three sentences and 150-1200 words in the rest, and that contain at least 6 sentences in total. In this way, we filter out i) articles with excessively long content to reduce memory consumption; ii) very short leading sentences with little information which are unlikely to be a good summary. To encourage the model to generate abstrative summaries, we also remove articles where any of the top three sentences is exactly repeated in the rest of the article.

Third, we try to remove articles whose top three sentences may not form a relevant summary.
[EX A]: What does the data cleaning and filtering process consist of?

[EX Q]: Next, we analyze why they do not perform well in this task and with this data:

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Noisy data. A major challenge of this task is the discrepancy between annotations due to subjective judgement. Although this affects also detection using only text, its repercussion is bigger in more complex tasks, such as detection using images or multimodal detection.

Complexity and diversity of multimodal relations. Hate speech multimodal publications employ a lot of background knowledge which makes the relations between visual and textual elements they use very complex and diverse, and therefore difficult to learn by a neural network.

Small set of multimodal examples. Fig. FIGREF5 shows some of the challenging multimodal hate examples that we aimed to detect. But although we have collected a big dataset of $150K$ tweets, the subset of multimodal hate there is still too small to learn the complex multimodal relations needed to identify multimodal hate.
[EX A]:
What is author's opinion on why current multimodal models cannot outperform models analyzing only text?