ParaBank: Monolingual Bitext Generation and Sentential Paraphrasingvia Lexically-constrained Neural Machine Translation
Table 4: Top: Semantic similarity between paraphrases and reference sentences, as scored by human annotators on a 0-100 scale (least to most similar). Results are grouped by length of reference sentences {5, 10, 20, 40}. System names and numbers correspond to Tab. 3. Improvements over ParaNMT (Czech-English only) in bold. Asterisk (*) indicates best in column. Bottom: Lexical diversity between generated paraphrases and reference sentences, as computed by a modified BLEU score with no length penalty. Results are grouped by length of reference sentences, and BLEU is computed over concatenated references and concatenated paraphrases. Lower BLEU scores indicate greater lexical divergence; the lowest per column (bottom half) is indicated by (*).
['[EMPTY]', '#', 'System', 'Czech-English len=5', 'Czech-English len=10', 'Czech-English len=20', 'Czech-English len=40', 'Czech-English Avg.', 'French-English len=5', 'French-English len=10', 'French-English len=20', 'French-English len=40', 'French-English Avg.']
The top half of Tab. The bottom half of Tab. For every length, there is at least one ParaBank system that exhibits higher lexical diversity than ParaNMT; unsurprisingly, the ParaBank systems that apply the greatest number of lexical constraints tend to yield the greatest lexical diversity (e.g., system 21).