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The study of question answering has received increasing attention in recentyears. This work focuses on providing an answer that compatible with both userintent and conditioning information corresponding to the question, such asdelivery status and stock information in e-commerce. However, these conditionsmay be wrong or incomplete in real-world applications. Although existingquestion answering systems have considered the external information, such ascategorical attributes and triples in knowledge base, they all assume that theexternal information is correct and complete. To alleviate the effect ofdefective condition values, this paper proposes condition aware and reviseTransformer (CAR-Transformer). CAR-Transformer (1) revises each condition valuebased on the whole conversation and original conditions values, and (2) itencodes the revised conditions and utilizes the conditions embedding to selectan answer. Experimental results on a real-world customer service datasetdemonstrate that the CAR-Transformer can still select an appropriate reply whenconditions corresponding to the question exist wrong or missing values, andsubstantially outperforms baseline models on automatic and human evaluations.The proposed CAR-Transformer can be extended to other NLP tasks which need toconsider conditioning information.

Correction of Faulty Background Knowledge based on Condition Aware and Revise Transformer for Question Answering