# Reference-based Weak Supervision for Answer Sentence Selection using Web Data

arXiv: https://arxiv.org/abs/2104.08943 · HTML: https://mlsanity.com/paper/2104.08943/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 11 of 11)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [The NarrativeQA Reading Comprehension Challenge](https://mlsanity.com/paper/1712.07040/) (2017)
- [Achieving Human Parity on Automatic Chinese to English News Translation](https://mlsanity.com/paper/1803.05567/) (2018)
- [TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection](https://mlsanity.com/paper/1911.04118/) (2019)
- [Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access](https://mlsanity.com/paper/2006.03533/) (2020)
- [Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning](https://mlsanity.com/paper/1805.09927/) (2018)
- [A Compare-Aggregate Model with Latent Clustering for Answer Selection](https://mlsanity.com/paper/1905.12897/) (2019)
- [LSTM-based Deep Learning Models for Non-factoid Answer Selection](https://mlsanity.com/paper/1511.04108/) (2015)
- [AVA: an Automatic eValuation Approach to Question Answering Systems](https://mlsanity.com/paper/2005.00705/) (2020)

## Built on by (most cited first, 1 of 1)

- [Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages](https://mlsanity.com/paper/2305.16302/) (2023)

## Abstract (arXiv)

Answer sentence selection (AS2) modeling requires annotated data, i.e., hand-labeled question-answer pairs. We present a strategy to collect weakly supervised answers for a question based on its reference to improve AS2 modeling. Specifically, we introduce Reference-based Weak Supervision (RWS), a fully automatic large-scale data pipeline that harvests high-quality weakly-supervised answers from abundant Web data requiring only a question-reference pair as input. We study the efficacy and robustness of RWS in the setting of TANDA, a recent state-of-the-art fine-tuning approach specialized for AS2. Our experiments indicate that the produced data consistently bolsters TANDA. We achieve the state of the art in terms of P@1, 90.1%, and MAP, 92.9%, on WikiQA.