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: We defined a sequence labeling task to extract custom entities from user input. We assumed seven (7) possible entities (see Table TABREF43) to be recognized by the model: topic, subtopic, examination mode and level, question number, intent, as well as the entity other for remaining words in the utterance. We defined a classification problem to predict the system's next action according to the given user input. We assumed 13 custom actions (see Table TABREF42) that we considered being our labels. In the conversational dataset, each input was automatically labeled by the rule-based system with the corresponding next action and the dialogue-id. Thus, no additional post-labeling was required.
Question: How does the IPA label data after interacting with users?

Output: It defined a sequence labeling task to extract custom entities from user input and label the next action (out of 13 custom actions defined).

Input: Consider Input: We evaluated the proposed model using the publicly available LibriSpeech ASR corpus BIBREF23. The LibriSpeech dataset consists of 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset.
Question: How big is LibriSpeech dataset?

Output: 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset

Input: Consider Input: For sentiment classification, we systematically study the effect of character-level adversarial attacks on two architectures and four different input formats. We also consider the task of paraphrase detection.
Question: What end tasks do they evaluate on?
Output: Sentiment analysis and paraphrase detection under adversarial attacks