You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.

Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.

Section Title:
introduction

Context before the citation:
Some methods are based on likelihood (Och and Ney, 2002; Blunsom et al., 2008), error rate (Och, 2003; Zhao and Chen, 2009; Pauls et al., 2009; Galley and Quirk, 2011), margin (Watanabe et al., 2007; Chiang et al., 2008) and ranking (Hopkins and May, 2011), and among which minimum error rate training (MERT) (Och, 2003) is the most popular one. h is a feature vector which is scaled by a weight W. Parameter estimation is one of the most important components in SMT, and various training methods have been proposed to tune W. where f and e (e') are source and target sentences, respectively.

Citation Sentence:
Some methods are based on likelihood ( Och and Ney , 2002 ; Blunsom et al. , 2008 ) , error rate ( Och , 2003 ; Zhao and Chen , 2009 ; Pauls et al. , 2009 ; Galley and Quirk , 2011 ) , margin ( Watanabe et al. , 2007 ; Chiang et al. , 2008 ) and ranking ( Hopkins and May , 2011 ) , and among which minimum error rate training ( MERT ) ( Och , 2003 ) is the most popular one .

Context after the citation:
All these training methods follow the same pipeline: they train only a single weight on a given development set, and then use it to translate all the sentences in a test set. We call them a global training method. One of its advantages is that it allows us to train a single weight offline and thereby it is efficient. However, due to the diversity and uneven distribution of source sentences(Li et al., 2010), there are some shortcomings in this pipeline.
Background