AAAI Press Formatting Instructions for Authors Using LaTeX — A Guide
Table 2: Translation results on small-scale balanced Zh⇒En multi-domain data used by Zeng et al. (2018). We also list the results of Zeng et al. (2018) on RNN-based NMT. “#M” denotes the number of required models and “#Para.” denotes the number of parameters. “+” denotes appending new features to the above row. “↑” indicates statistically significant difference (p<0.01) from “Transformer” in the corresponding domains.
['[BOLD] #', '[BOLD] Architecture', '[BOLD] #M', '[BOLD] #Para.', '[BOLD] BLEU Law', '[BOLD] BLEU Oral', '[BOLD] BLEU Thesis', '[BOLD] BLEU News', '[BOLD] BLEU Avg.', '[BOLD] BLEU △']
As seen, the proposed models significantly and incrementally improve the translation quality in all cases, although there are considerable differences among different scenarios. The fine-tuning approaches (Rows 4-5) achieve significant improvements over the Transformer baseline. We attribute this to the facts that 1) the fine-tuning maintains a distinct model for each domain; and 2) there are sufficient data in each target domain. The unified models are directly comparable to our approach.