# Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation

arXiv: https://arxiv.org/abs/2106.11096 · HTML: https://mlsanity.com/paper/2106.11096/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## 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)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection](https://mlsanity.com/paper/1911.04118/) (2019)
- [ANTIQUE: A Non-Factoid Question Answering Benchmark](https://mlsanity.com/paper/1905.08957/) (2019)
- [Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering](https://mlsanity.com/paper/1707.07847/) (2017)
- [A Compare-Aggregate Model with Latent Clustering for Answer Selection](https://mlsanity.com/paper/1905.12897/) (2019)
- [MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching](https://mlsanity.com/paper/1905.10289/) (2019)
- [Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture](https://mlsanity.com/paper/1707.06372/) (2017)
- [aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model](https://mlsanity.com/paper/1801.01641/) (2018)
- [Training Curricula for Open Domain Answer Re-Ranking](https://mlsanity.com/paper/2004.14269/) (2020)
- [Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling](https://mlsanity.com/paper/2006.15599/) (2020)
- [Question Answering through Transfer Learning from Large Fine-grained Supervision Data](https://mlsanity.com/paper/1702.02171/) (2017)
- [Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge](https://mlsanity.com/paper/2104.05216/) (2021)

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

- [Intent-calibrated Self-training for Answer Selection in Open-domain Dialogues](https://mlsanity.com/paper/2307.06703/) (2023)

## Abstract (arXiv)

In this work, we propose a novel and easy-to-apply data augmentation strategy, namely Bilateral Generation (BiG), with a contrastive training objective for improving the performance of ranking question answer pairs with existing labeled data. In specific, we synthesize pseudo-positive QA pairs in contrast to the original negative QA pairs with two pre-trained generation models, one for question generation, the other for answer generation, which are fine-tuned on the limited positive QA pairs from the original dataset. With the augmented dataset, we design a contrastive training objective for learning to rank question answer pairs. Experimental results on three benchmark datasets show that our method significantly improves the performance of ranking models by making full use of existing labeled data and can be easily applied to different ranking models.