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Textual content around us is growing on a daily basis. Numerous articles arebeing written as we speak on online newspapers, blogs, or social media.Similarly, recent advances in the AI field, like language models or traditionalclassic AI approaches, are utilizing all the above to improve their learnedrepresentation to tackle NLP challenges with human-like accuracy. It iscommonly accepted that it is crucial to have access to well-written text fromvalid sources to tackle challenges like text summarization, question-answering,machine translation, or even pronoun resolution. For instance, to summarizewell, one needs to select the most important sentences in order to concatenatethem to form the summary. However, what happens if we do not have access towell-formed English sentences or even non-valid sentences? Despite theimportance of having access to well-written sentences, figuring out ways tovalidate them is still an open area of research. To address this problem, wepresent a simplified way to validate English sentences through a novelneural-symbolic approach. Lately, neural-symbolic approaches have triggered anincreasing interest towards tackling various NLP challenges, as they aredemonstrating their effectiveness as a central component in various AI systems.Through combining Classic with Modern AI, which involves the blending ofgrammatical and syntactical rules with language models, we effectively tacklethe Corpus of Linguistic Acceptability (COLA), a task that shows whether or nota sequence of words is an English grammatical sentence. Among others,undertaken experiments effectively show that blending symbolic and non-symbolicsystems helps the former provide insights about the latter's accuracy results.

A Neural-Symbolic Approach Towards Identifying Grammatically Correct Sentences