Triple-to-Text: Converting RDF Triples into High-Quality Natural Languages via Optimizing an Inverse KL Divergence
Table 3. Comparison of model performance.
['[EMPTY]', 'BLEU-3 ↑ WebNLG', 'BLEU-3 ↑ SemEval', 'BLEU-3 ↑ SKE', 'BLEU-4 ↑ WebNLG', 'BLEU-4 ↑ SemEval', 'BLEU-4 ↑ SKE', 'TER ↓ WebNLG', 'TER ↓ SemEval', 'TER ↓ SKE', 'METEOR ↑ WebNLG', 'METEOR ↑ SemEval', 'METEOR ↑ SKE']
From the results, we find that our proposed T2T method improves the quality of the generated sentence on these word-based metrics. As we have analyzed, generators optimized via inverse KL divergence tend to generate text with more common expressions, while other baselines tend to use some low-quality text. Thus, sentences from T2T will overlap more words with reference text, which means it can achieve better performance on the word based metrics like BLEU.