| Models 2.1 Baseline Models | Table 1 shows how morphemes are being used in the MT system . |
| Models 2.1 Baseline Models | The first phase of our morphology prediction model is to train a MT system that produces morphologically simplified word forms in the target language. |
| Models 2.1 Baseline Models | MT System Alignment: |
| Machine Translation as a Decipherment Task | MT Systems: We build and compare different MT systems under two training scenarios: |
| Machine Translation as a Decipherment Task | Evaluation: All the MT systems are run on the Spanish test data and the quality of the resulting English translations are evaluated using two different measures—(1) Normalized edit distance score (Navarro, 2001),6 and (2) BLEU (Papineni et |
| Machine Translation as a Decipherment Task | Results: Figure 3 compares the results of various MT systems (using parallel versus decipherment training) on the two test corpora in terms of edit distance scores (a lower score indicates closer match to the gold translation). |
| Abstract | As MT systems improve, the shortcomings of the n-gram based evaluation metrics are becoming more apparent. |
| Abstract | State-of-the-art MT systems are often able to output fluent translations that are nearly grammatical and contain roughly the correct words, but still fail to eXpress meaning that is close to the input. |
| Abstract | , 2006) is more adequacy-oriented, it is only employed in very large scale MT system evaluation instead of day-to-day research activities. |