Entity-aware answer sentence selection for question answering with transformer-based language models

The Answer Sentence Selection (AS2) task is defined as the task of ranking the candidate answers for each question based on a matching score. The matching score is the probability of being a correct answer for a given question. Detecting the question class and matching it with the named entities of the answer sentence to narrow down the search space was used in primary question answering systems. We used this idea in the state-of-the-art text matching models namely, Transformer-based language models. In this paper, we proposed two different architectures: Ent-match and Ent-add, while using two different question classifiers: Convolutional Neural Network-based (CNN-based) and rule-based. The proposed models outperform the state-of-the-art AS2 model, namely TANDA and RoBERTa-base on both TREC-QA and Wiki-QA datasets. Using Wiki-QA, the Ent-add (CNN-based) model outperforms the TANDA model by 2.1% and 1.9% improvement over Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) metrics, respectively. Over the TREC-QA dataset the Ent-match (CNN-based) model outperformed the TANDA model with 1.5% and 1.4% improvement over MAP and MRR, respectively.