add tense transform #135
add tense transform #135
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| super().__init__() | ||
| assert to_tense in ['past', 'present', 'future', 'random'] | ||
| self.to_tense = to_tense | ||
| self.nlp = spacy.load('en_core_web_sm') |
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from initialize import spacy_nlp
self.spacy_model = spacy_nlp if spacy_nlp else spacy.load("en_core_web_sm")
| doc = self.nlp(text) | ||
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| out = list() | ||
| out.append(doc[0].text) |
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Why not change the tense of all sentences?
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There seems to be a misunderstanding , we are indeed changing the tense of all sentences, here the variable out is just initialised with the first word(token) in the given sentence, a few lines below for word in doc we iterate over all words in the sentence
| return newWord | ||
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| ''' | ||
| change tense function borrowed from https://github.com/bendichter/tenseflow/blob/master/tenseflow/change_tense.py |
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Please add this in the README too.
| "to_tense": "past" | ||
| }, | ||
| "inputs": { | ||
| "sentence": "I will go to the park." |
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Since the transformation contains a lot of logic, can you also add longer sentences too (containing more than 1-verb) and also add cases where the transformation works great and where it does not.
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Thanks for pointing this out , we have added a few more test cases to help provide a better picture
| tasks = [ | ||
| TaskType.TEXT_CLASSIFICATION, | ||
| TaskType.TEXT_TO_TEXT_GENERATION, | ||
| TaskType.TEXT_TAGGING, |
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Tagging would not be applicable since the number of tokens would change.
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| def generate(self, sentence: str): | ||
| perturbed_texts = self.change_tense(sentence, to_tense = random.choice(['past', 'present', 'future']) if self.to_tense == 'random' else self.to_tense) | ||
| return [perturbed_texts] |
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Please add a newline.
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This looks fine overall. Please address the comments! Thank you very much for your efforts! |
Thanks for your review @kaustubhdhole |
| } | ||
| ``` | ||
| ### Data and Source Code | ||
| change tense and verb infliction borrowed from https://github.com/bendichter/tenseflow |
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Thank you @tanay2001! Since some of the code was derived from this work, I suggest inviting the authors of this repository to be included as the authors of this transformations. Have you reached out to them?
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Hi @tanay2001, thanks for addressing the concerns. I think this is good to go. Just one minor thing - please reach out to the authors of the repo once to invite them :) Thanks again for your changes! |
This transformation converts sentences from one tense to the other, example: simple present to simple past.