T5-base model create spelling mistake is summary
Hi,I am using T5-base model for abstractive summarization, results are good but I am getting newly generated spelling mistakes in the summary which were not actually present in input text.Can anyone tell me why these spelling mistakes occuring and how can I solve this?
2020-10-24T20:02:57Z
I think it’s due to your min_output size, for example if you have forced the model to generate results at minimum more than 50 sequence, and somehow the prediction length predicted only 40 sequences, I think it will start to generate  random tokens just to reach the 50 seq.
2020-11-05T08:42:40Z
Hi Zack, hope you are doing well !!Thank you for your reply.Actually it is not generating random tokens, but it is misspelling them.For e.g a word “productive” in input text is spelled as “priductive”