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:
None

Context before the citation:
It maximizes the probability of getting the entire DA sequence correct, but it does not necessarily find the DA sequence that has the most DA labels correct (Dermatas and Kokkinakis 1995). The combination of likelihood and prior modeling, HMMs, and Viterbi decoding is fundamentally the same as the standard probabilistic approaches to speech recognition (Bahl, Jelinek, and Mercer 1983) and tagging (Church 1988). Dialogue Act Modeling sequence with the highest posterior probability:

Citation Sentence:
It maximizes the probability of getting the entire DA sequence correct , but it does not necessarily find the DA sequence that has the most DA labels correct ( Dermatas and Kokkinakis 1995 ) .

Context after the citation:
To minimize the total number of utterance labeling errors, we need to maximize the probability of getting each DA label correct individually, i.e., we need to maximize P(U11E) for each i = 1,. . . ,n. We can compute the per-utterance posterior DA probabilities by summing: where the summation is over all sequences U whose ith element matches the label in question. The summation is efficiently carried out by the forward-backward algorithm for HMMs (Baum et al. 1970).3 For zeroth-order (unigram) discourse grammars, Viterbi decoding and forwardbackward decoding necessarily yield the same results.
Background