# In Situ Answer Sentence Selection at Web-scale

arXiv: https://arxiv.org/abs/2201.05984 · HTML: https://mlsanity.com/paper/2201.05984/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 16 Jan 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection](https://mlsanity.com/paper/1911.04118/) (2019)
- [Learning a Deep Listwise Context Model for Ranking Refinement](https://mlsanity.com/paper/1804.05936/) (2018)
- [NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned](https://mlsanity.com/paper/2101.00133/) (2021)
- [Hierarchical Question Answering for Long Documents](https://mlsanity.com/paper/1611.01839/) (2016)
- [Semi-supervised Multitask Learning for Sequence Labeling](https://mlsanity.com/paper/1704.07156/) (2017)
- [A Compare-Aggregate Model with Latent Clustering for Answer Selection](https://mlsanity.com/paper/1905.12897/) (2019)
- [Will this Question be Answered? Question Filtering via Answer Model Distillation for Efficient Question Answering](https://mlsanity.com/paper/2109.07009/) (2021)
- [Joint Models for Answer Verification in Question Answering Systems](https://mlsanity.com/paper/2107.04217/) (2021)
- [R3: Reinforced Reader-Ranker for Open-Domain Question Answering](https://mlsanity.com/paper/1709.00023/) (2017)
- [End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training](https://mlsanity.com/paper/2012.01414/) (2020)
- [ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition](https://mlsanity.com/paper/1911.10436/) (2019)

## Built on by (most cited first, 2 of 2)

- [Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch](https://mlsanity.com/paper/2403.14729/) (2024)
- [QUADRo: Dataset and Models for QUestion-Answer Database Retrieval](https://mlsanity.com/paper/2304.01003/) (2023)

## Abstract (arXiv)

Current answer sentence selection (AS2) applied in open-domain question answering (ODQA) selects answers by ranking a large set of possible candidates, i.e., sentences, extracted from the retrieved text. In this paper, we present Passage-based Extracting Answer Sentence In-place (PEASI), a novel design for AS2 optimized for Web-scale setting, that, instead, computes such answer without processing each candidate individually. Specifically, we design a Transformer-based framework that jointly (i) reranks passages retrieved for a question and (ii) identifies a probable answer from the top passages in place. We train PEASI in a multi-task learning framework that encourages feature sharing between the components: passage reranker and passage-based answer sentence extractor. To facilitate our development, we construct a new Web-sourced large-scale QA dataset consisting of 800,000+ labeled passages/sentences for 60,000+ questions. The experiments show that our proposed design effectively outperforms the current state-of-the-art setting for AS2, i.e., a point-wise model for ranking sentences independently, by 6.51% in accuracy, from 48.86% to 55.37%. In addition, PEASI is exceptionally efficient in computing answer sentences, requiring only ~20% inferences compared to the standard setting, i.e., reranking all possible candidates. We believe the release of PEASI, both the dataset and our proposed design, can contribute to advancing the research and development in deploying question answering services at Web scale.