Thank you and this is very helpful! I think under some circumstances your work is better than the one presented from that article. When solving this type of problem it is not necessary to include every individual input keyword or multiword expression, as long as the model comprehend and make a reasonable output based on tokens with similar meaning. I do have a few more questions if you don’t mind:


Did you use the identical web-nlg-2020 dataset from this article? If not, would you reveal the type/domain of the training dataset you used?


What is the difference between the ForceWords, ForceWords2 and ForceWordArvix models under your profile? I tried all of them and prefer ForceWords for now.


Is there a specific reason why your input from your shared demo is in the form of
“”"[‘At the core’, ‘mismanagement of the Coronavirus’, ‘lies its distrust of science’] At""", with 3 sets of quotations? I do understand that you are including a string of list as input words, with “At” as an “anchor” that starts the sentence. I am also not sure why in the output, the model would provided an additional set of input that is different but somehow related to the original input, along with a new output, even though I already set number of returned sequence to 1.


Is there anything similar to a “seed” that we can set for output? When I ran your model with the identical input, some outputs were extremely outstanding and I wish I could’ve saved it.


Thank you!