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.

Ex 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?

Ex 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).

Ex Input:
As explained in Section SECREF15 , the corruption introduced in Doc2VecC acts as a data-dependent regularization that suppresses the embeddings of frequent but uninformative words. In contrast, Doc2VecC manages to clamp down the representation of words frequently appear in the training set, but are uninformative, such as symbols and stop words.
Question: How do they determine which words are informative?

Ex Output:
Informative are those that will not be suppressed by regularization performed.

Ex Input:
Some authors use pretrained embeddings (especially when their data set is too small to train their own embeddings) or try to modify these embeddings and adjust to their set. But the biggest drawback of these approaches is that the corpus for training embeddings can be not related to the specific task where embeddings are utilized. A lot of medical concepts are not contained in well-known embeddings bases. Furthermore, the similarity of words may vary in different contexts. Then, we compute embeddings of concepts (by GloVe) for interview descriptions and for examination descriptions separately.
Question: Do they fine-tune the used word embeddings on their medical texts?

Ex Output:
No