Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization
Table 4: Comparison of different models on the French WikiBio dataset
['[BOLD] Model', '[BOLD] BLEU-4', '[BOLD] NIST-4', '[BOLD] ROUGE-4']
Note that the code of Lebret et al. We observe that our final model gives the best performance - though the bifocal attention model performs poorly as compared to the basic seq2seq model on French. However, the overall performance for French and German are much smaller than those for English. There could be multiple reasons for this. First, the amount of training data in these two languages is smaller than that in English. Specifically, the amount of training data available in French (German) is only 24.2 (7.5)% of that available for English. Second, on average the descriptions in French and German are longer than that in English (EN: 26.0 words, FR: 36.5 words and DE: 32.3 words). Finally, a manual inspection across the three languages suggests that the English descriptions have a more consistent structure than the French descriptions. For example, most English descriptions start with name followed by date of birth but this is not the case in French. However, this is only a qualitative observation and it is hard to quantify this characteristic of the French and German datasets. Due to lack of space, we do not show similar plots for French and German but we would like to mention that, in general, the differences between the attention weights learned by the model with and without gated orthogonalization were more pronounced for the French/German dataset than the English dataset.