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.
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Question: We are in line with recent work BIBREF16 , proposing to shift evaluation from absolute values to more exploratory evaluations focusing on weaknesses and strengths of the embeddings and not so much in generic scores. For example, one metric could consist in checking whether for any given word, all words that are known to belong to the same class are closer than any words belonging to different classes, independently of the actual cosine.

Answer: What new metrics are suggested to track progress?


Question: Car-speak is abstract language that pertains to a car's physical attribute(s). In this instance the physical attributes that the term “fast” pertains to could be the horsepower, or it could be the car's form factor (how the car looks). However, we do not know exactly which attributes the term “fast” refers to.

Answer: How does car speak pertains to a car's physical attributes?


Question: Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin.  Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks.

Answer:
What are the specific tasks being unified?