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:
conclusion

Context before the citation:
Our most accurate single grammar achieves an F score of 91.6 on the WSJ test set, rivaling discriminative reranking approaches (Charniak and Johnson, 2005) and products of latent variable grammars (Petrov, 2010), despite being a single generative PCFG. Second, the diversity of the individual grammars controls the gains that can be obtained by combining multiple grammars into a product model. First, the accuracy of the model used for parsing the unlabeled data is important for the accuracy of the resulting single self-trained grammars.

Citation Sentence:
Our most accurate single grammar achieves an F score of 91.6 on the WSJ test set , rivaling discriminative reranking approaches ( Charniak and Johnson , 2005 ) and products of latent variable grammars ( Petrov , 2010 ) , despite being a single generative PCFG .

Context after the citation:
Our most accurate product model achieves an F score of 92.5 without the use of discriminative reranking and comes close to the best known numbers on this test set (Zhang et al., 2009). In future work, we plan to investigate additional methods for increasing the diversity of our selftrained models. One possibility would be to utilize more unlabeled data or to identify additional ways to bias the models. It would also be interesting to determine whether further increasing the accuracy of the model used for automatically labeling the unlabeled data can enhance performance even more.
CompareOrContrast