Your task is to classify the citation intent within the following provided text from a computational linguistics research paper. The cited work is demarcated by "<cite>" and "</cite>". Determine the purpose of the cited work by selecting from the listed categories:

- Background: The cited paper underpins the subject matter.
- Motivation: The cited paper inspires or provides a rationale for the current research.
- Uses: The current work utilizes concepts or tools from the cited paper.
- Extends: The current work advances ideas or methods from the cited paper.
- Similarities: The current work identifies commonalities with the cited paper.
- Differences: The current work delineates its distinction from the cited paper.
- FutureWork: The cited paper is acknowledged as groundwork for prospective research.

Indicate the intents by listing them in a `json` array, e.g. ["Background", "Uses"]. More than one intent may be applicable. Do not include any extraneous text in your response.

Context with Citation:
In our first experiment, we examine the utility of a QA dataset in relation to the slot-filling task of <cite>Levy et al. (2017)</cite> . <cite>Their</cite> zero-shot model generalised from seen relations to unseen relations by translating all relations into natural language question templates, such as Where was XXX born? for the relation place of birth. Identifying an instance of such a relation in text is then equivalent to finding an answer to the relevant question template, instantiated with the appropriate entity. However, such a model also needs to be able to identify when no answer is found in the text, and to achieve this <cite>they</cite> trained a slightly modified version of BiDAF (Seo et al., 2016) (Rajpurkar et al., 2016) , can be applied to the relation extraction task.
["Background"]