Results on Chinese-English Alignment Sample Clauses

Results on Chinese-English Alignment. Table 3 shows the results on the Chinese-English word align- ment task. We used the TSINGHUAALIGNER evaluation dataset [Xxx and Xxx, 2015] in which both the validation and test sets contain 450 manually-aligned Chinese-English sentence pairs. We follow Xxxxx et al. [2015b] to “force- decode” our jointly trained models to produce translations that match the references. Then, we extract only one-to- one alignments by selecting the source word with the highest alignment weight for each target word. We find that agreement-based joint training significantly reduces alignment errors for both directions as compared with independent training. This suggests that introducing agree- ment does enable NMT to capture attention more accurately and thus lead to better translations. Figure 2(b) shows exam- ple alignment matrices resulted from agreement-based joint training. However, the error rates in Table 3 are still higher than con- ventional aligners that can achieve an AER around 30 on the 2The scores for E → C is much lower than C → E because BLEU Word Type Freq. Indep. Joint to preposition high 2.21 1.80 and conjunction high 2.21 1.60 the definite article high 1.96 1.56 yesterday noun medium 2.04 1.55 actively adverb medium 1.90 1.32 festival noun medium 1.55 0.85 inspects verb low 0.29 0.02 rebellious adjective low 0.29 0.02 noticing verb low 0.19 0.01 Table 4: Comparison of independent and joint training in terms of average attention entropy (see Eq. (15)) on Chinese- to-English translation. same dataset. There is still room for improvement in attention accuracy.
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