text: 478,92,1438543852109897734,2853379350,Siddhant Garg,"🚨#EMNLP2021 Paper New💡for Efficient QA➡️ Filter questions that will not be answered by QA system Interesting 🔎: Transformer-based QA scores can be approximated only using question text via ""partial-input distillation"" @AmazonScience @amoschitti1 📰: <LINK> <LINK> @AmazonScience @amoschitti1 Practical QA systems operate at high Prec. (for customer req.) and end up not answering a large % of ques by failing system threshold on answer confidence score We train filters to preemptively remove ques that are not answered by the system, saving (retrieval+answering) compute @AmazonScience @amoschitti1 We propose 2 loss objectives for learning 2 types of filters: regression & classification head by distilling knowledge of QA system scores Training does not require any human labels, only system generated scores Different from KD since teacher & student use different inputs <LINK> @AmazonScience @amoschitti1 Experimental results show:  (i) Question filters can approximate Pr/Re of QA system very well (ii) Filters can provide large efficiency gains, with only a small drop in Recall (user-tunable tradeoff) <LINK> @AmazonScience @amoschitti1 Code to be released soon at <LINK> Paper #AmazonScience: <LINK> Feel free to reach out to us in case of any questions 😀",https://arxiv.org/abs/2109.07009,"In this paper we propose a novel approach towards improving the efficiency of Question Answering (QA) systems by filtering out questions that will not be answered by them. This is based on an interesting new finding: the answer confidence scores of state-of-the-art QA systems can be approximated well by models solely using the input question text. This enables preemptive filtering of questions that are not answered by the system due to their answer confidence scores being lower than the system threshold. Specifically, we learn Transformer-based question models by distilling Transformer-based answering models. Our experiments on three popular QA datasets and one industrial QA benchmark demonstrate the ability of our question models to approximate the Precision/Recall curves of the target QA system well. These question models, when used as filters, can effectively trade off lower computation cost of QA systems for lower Recall, e.g., reducing computation by ~60%, while only losing ~3-4% of Recall. ","Will this Question be Answered? Question Filtering via Answer Model