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Pairwise Neural Machine Translation Evaluation

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Paper from Francisco Guzman, Shafiq Joty, Lluis Marquez and Preslav Nakov
Ivan Zapreev
Mind Map by Ivan Zapreev, updated more than 1 year ago
Ivan Zapreev
Created by Ivan Zapreev over 9 years ago
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Resource summary

Pairwise Neural Machine Translation Evaluation
  1. Introduction
    1. Automated Machine Translation (MT)
      1. Evaluation
        1. Needed
          1. Developing a new MT
            1. Comparing two MT
            2. Reference based MT
              1. Comparing the system output to one or more human reference tranlations
                1. Most Common
                  1. Compute
                    1. Absolute quality score
                      1. Computing similarity between the machine and human translation
                      2. Simplest case
                        1. Computing word N-gram matches between the translation and the reference
                          1. BLUE
                        2. More advanced
                          1. Take into account various aspects of linguistic similarity
                            1. Better correlation with human judjment
                      3. Human ranking
                        1. Can be used to train automatic metrics
                          1. Can be oriented to predict absolute scores
                            1. Using
                              1. Regression
                                1. ?
                                2. Ranking
                                  1. Special case
                                    1. Compare two hypotheses and referenec
                                      1. Decide which hypothesis is better
                                        1. Recent result
                                          1. Guzman
                                            1. Learning framework
                                              1. Using
                                                1. Preference kernel
                                                  1. ?
                                                  2. Vector machines (SVM)
                                                    1. Syntactic structures
                                                      1. Discourage-based structures
                                                      2. High computational costs
                                                        1. Training
                                                          1. Testing
                                                            1. Due to
                                                              1. Using convolution kernels
                                                                1. ?
                                                                2. Over complex structures
                                                                3. Simplification is needed!
                                    2. Research
                                      1. Framework for machine translation evaluation
                                        1. Novel!
                                          1. Goal
                                            1. Select a better translation from a pair of hypothesis, given the reference translation
                                            2. Using
                                              1. Neural networks
                                                1. Multi-layer
                                                  1. Input layer
                                                    1. Semantic info
                                                      1. Syntactic info
                                                        1. Lexical info
                                                        2. Hidden layer
                                                          1. Captures the interactions between the relevant input components
                                                        3. Models the interaction between
                                                          1. The two hypothesis
                                                            1. Reference and Hypothesis
                                                          2. Distributed vector representations
                                                            1. Used for storing
                                                              1. The Two hypothesis
                                                              2. Based on
                                                                1. Word embedding
                                                                  1. Sentence embedding
                                                                  2. Learned from
                                                                    1. Neural Networks
                                                                      1. Novel!
                                                                        1. Can be trained to optimize task-specific cost function
                                                                      2. Efficient
                                                                        1. Vector-based compression
                                                                          1. ?
                                                                    2. Experiments
                                                                      1. WMT12 metrics task
                                                                        1. Better results than by Guzman
                                                                          1. High correlation with human judjment
                                                                            1. Comparable with the best!
                                                                              1. DiscoTK
                                                                                1. Metric
                                                                                  1. Combination based
                                                                                    1. Much heavier
                                                                              2. Embeddings
                                                                                1. Syntactically oriented
                                                                                  1. Semantically oriented
                                                                                    1. Cumulative performance gains
                                                                                      1. Over
                                                                                        1. BLUE
                                                                                          1. NIST
                                                                                            1. METEOR
                                                                                              1. TER
                                                                                      2. Simplification is needed!
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