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I have a big database that contains two table CLIENT_DESCRIPTION and REPORTER_DESCRIPTIPN. This database is for a study on how people answer a specific question How do you describe your experience with our product in your country?.

For every client in this questionnaire, a client will provide us with a paragraph. And a reporter listening to the client should provide us a paragraph containing no more than 3 sentences that shorts the client opinion. The reporter will write the description as he understand it from the client.

For example:
Client description: Your product is very good compared to other similar products. I can found it easily in the store, and the price is accepted. I think that the cover color is very bright. I guess you should use a dark color instead. The product broke with me once, is it possible to fix it?
Reporter description: The client lives in LA. The sales man should contact him to promote our premium plan. He want to have a dark copy of the product cover. he has a broken one and needs to fix it.

I have thousands of similar entries in my two table, and for 70% of them, every client entry is matched with it's reporter entry.

Is it possible to train the dataset to match the remaining entries?

70% of the entries are matched(Client descriptions matched with their equivalent reporters descriptions).
We have 30% of client descriptions entries from CLIENT_DESCRIPTION that for some reason are not matched with their equivalent reporter description from REPORTER_DESCRIPTIPN.

What algorithm or library I can use to train the already matched entries to match the unmatched ones.

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