SlowGuess/ACL_2020_OCR huggingface dataset:
Table 8: Qualitative examples from IWSLT German-English translation. Numbers inside the parenthesis are sentence-level BLEU scores. Red word is where the baseline Transformer makes a mistake without considering the possible future phrase and fails to recover. On the other hand, our model makes the right decision at the blue word, hence generates more coherent sentence. Please refer to Section 4.6 in the main paper for detailed explanation.
Reference	china offers tax exemptions for laid-off workers
Transformer	china encourages laid-off workers to seek employment
Ours	china offers tax exemptions to laid-off workers
Reference	swiss police arrest Britons who allegedly ran rental car ratchet
Transformer	three Britons arrested in swiss luxury hotel
Ours	swiss police arrest three Britons in rental car ratchet case
Reference	south korea stocks extend declines as kia concerns intensify
Transformer	south korean stocks fall for #th time in # days ; kia leads
Ours	south korean stocks fall as kia troubles intensify
Table 9: Qualitative examples from the Gigaword summarization dataset. Baseline model suffers from early mistakes. Our model generates more coherent summaries.
7905