# Context-Aware Answer Extraction in Question Answering Source: [Context-Aware Answer Extraction in Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:42:59.203Z Authors: Yeon Seonwoo, Ji-Hoon Kim, Jung-Woo Ha, Alice Oh Published: 2020-11-05T07:10:08.000Z ## Abstract Extractive QA models have shown very promising performance in predicting the correct answer to a question for a given passage. However, they sometimes result in predicting the correct answer text but in a context irrelevant to the given question. This discrepancy becomes especially important as the number of occurrences of the answer text in a passage increases. To resolve this issue, we propose \\textbf{BLANC} (\\textbf{BL}ock \\textbf{A}ttentio\\textbf{N} for \\textbf{C}ontext prediction) based on two main ideas: context prediction as an auxiliary task in multi-task learning manner, and a block attention method that learns the context prediction task. With experiments on reading comprehension, we show that BLANC outperforms the state-of-the-art QA models, and the performance gap increases as the number of answer text occurrences increases. We also conduct an experiment of training the models using SQuAD and predicting the supporting facts on HotpotQA and show that BLANC outperforms all baseline models in this zero-shot setting. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()