@vdw and @Nikronic thank you 
my point is in some tasks may I need to process text sequentially right-to-left and left-to-right like Grammer Error Correction (GEC) tasks. To extract features and relationship between words it will be better to do rtl and ltr processing. some types of errors are easier to be corrected by a right-to-left seq2seq model, while some are more likely to be corrected by a left-to-right seq2seq model.
for example, if I have this sentence
"She likes playing in park and come here every week "
so, when I start from right-to-left I can easily correct an error like,
"She likes playing in the park and come here every week "
when I reverse the process from left-to-right, I will fix correctly an error like:
“She likes playing in the park and comes here every week”
from this point, I need to apply the both approaches to get better results, and this is my question. Please, check the below link:

  
      
      GroundAI
  
  
    

Reaching Human-level Performance in Automatic Grammatical Error
  Correction:... 2

Neural sequence-to-sequence (seq2seq) approaches have proven to be successful in grammatical error correction (GEC). Based on the seq2seq framework, we propose a novel fluency boost learning and inference mechanism. Fluency boosting learning...