TIGS: An Inference Algorithm for Text Infilling with Gradient Search
Table 1: BLEU and NLL results.
['Datasets', 'Metrics', 'Methods', '[ITALIC] r=25% Random', '[ITALIC] r=25% Middle', '[ITALIC] r=50% Random', '[ITALIC] r=50% Middle', '[ITALIC] r=75% Random', '[ITALIC] r=75% Middle']
Generally, we find that bidirectional methods (BiRNN-BiBS, BiRNN-GSN, and Seq2Seq-f+b) outperform unidirectional ones (Seq2Seq-f and Seq2Seq-b) in most cases. The model-based methods (Mask-Seq2Seq and Mask-Self-attn) perform well on unconditional text infilling task (APRC), but slightly poorly on conditional text infilling tasks (Dialog and Poetry). In line with the evaluation results in \newcitezhu2019text, the Mask-Self-attn performs consistently better than Mask-Seq2Seq. It has also achieved the highest BLEU score in some cases of unconditional text infilling tasks. However, in most cases of conditional text infilling tasks, the proposed method performs better than Mask-Self-attn.