# Joint Models for Answer Verification in Question Answering Systems

arXiv: https://arxiv.org/abs/2107.04217 · HTML: https://mlsanity.com/paper/2107.04217/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 9 Jul 2021.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [The Fact Extraction and VERification (FEVER) Shared Task](https://mlsanity.com/paper/1811.10971/) (2018)
- [Fine-grained Fact Verification with Kernel Graph Attention Network](https://mlsanity.com/paper/1910.09796/) (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)
- [A Compare-Aggregate Model with Latent Clustering for Answer Selection](https://mlsanity.com/paper/1905.12897/) (2019)
- [SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums](https://mlsanity.com/paper/1906.01727/) (2019)
- [Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification](https://mlsanity.com/paper/1805.02220/) (2018)
- [Adaptive Document Retrieval for Deep Question Answering](https://mlsanity.com/paper/1808.06528/) (2018)
- [Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension](https://mlsanity.com/paper/1906.04618/) (2019)
- [Fact Checking in Community Forums](https://mlsanity.com/paper/1803.03178/) (2018)
- [A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection](https://mlsanity.com/paper/2003.02349/) (2020)
- [TMLab SRPOL at SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums](https://mlsanity.com/paper/1906.01515/) (2019)
- [MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection](https://mlsanity.com/paper/2010.04970/) (2020)

## Built on by (most cited first, 13 of 13)

- [CABINET: Content Relevance based Noise Reduction for Table Question Answering](https://mlsanity.com/paper/2402.01155/) (2024)
- [Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch](https://mlsanity.com/paper/2403.14729/) (2024)
- [SmartBook: AI-Assisted Situation Report Generation for Intelligence Analysts](https://mlsanity.com/paper/2303.14337/) (2023)
- [Paragraph-based Transformer Pre-training for Multi-Sentence Inference](https://mlsanity.com/paper/2205.01228/) (2022)
- [Question-Answer Sentence Graph for Joint Modeling Answer Selection](https://mlsanity.com/paper/2203.03549/) (2022)
- [Knowledge Transfer from Answer Ranking to Answer Generation](https://mlsanity.com/paper/2210.12865/) (2022)
- [QUADRo: Dataset and Models for QUestion-Answer Database Retrieval](https://mlsanity.com/paper/2304.01003/) (2023)
- [Double Retrieval and Ranking for Accurate Question Answering](https://mlsanity.com/paper/2201.05981/) (2022)
- [Learning Answer Generation using Supervision from Automatic Question Answering Evaluators](https://mlsanity.com/paper/2305.15344/) (2023)
- [In Situ Answer Sentence Selection at Web-scale](https://mlsanity.com/paper/2201.05984/) (2022)
- [Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages](https://mlsanity.com/paper/2305.16302/) (2023)
- [SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative References](https://mlsanity.com/paper/2309.12250/) (2023)
- [Question-Context Alignment and Answer-Context Dependencies for Effective Answer Sentence Selection](https://mlsanity.com/paper/2306.02196/) (2023)

## Abstract (arXiv)

This paper studies joint models for selecting correct answer sentences among the top k provided by answer sentence selection (AS2) modules, which are core components of retrieval-based Question Answering (QA) systems. Our work shows that a critical step to effectively exploit an answer set regards modeling the interrelated information between pair of answers. For this purpose, we build a three-way multi-classifier, which decides if an answer supports, refutes, or is neutral with respect to another one. More specifically, our neural architecture integrates a state-of-the-art AS2 model with the multi-classifier, and a joint layer connecting all components. We tested our models on WikiQA, TREC-QA, and a real-world dataset. The results show that our models obtain the new state of the art in AS2.