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:
One common approach to measuring compositionality is to compare distributional representations of an MWE and its component words (e.g., Schone and Jurafsky, 2001; Baldwin et al., 2003; Katz and Giesbrecht, 2006; Reddy et al., 2011; Schulte im Walde et al., 2013; <cite>Salehi et al., 2015</cite>) . One issue faced by <cite>such approaches</cite> is that token-level instances of MWEs must be identified in a corpus in order to form distributional representations of them. Although word-level language models are widely used, and their performance can be higher than character-level language models, character-level models have the advantage that they can model out-of-vocabulary words (Mikolov et al., 2012) . In this paper we consider whether character-level neural network language models capture knowledge of MWE compositionality.
["Motivation"]