# Semi-supervised Question Retrieval with Gated Convolutions

arXiv: https://arxiv.org/abs/1512.05726 · HTML: https://mlsanity.com/paper/1512.05726/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2015.

## Built on by (most cited first, 28 of 28)

- [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning](https://mlsanity.com/paper/2104.06979/) (2021)
- [Contrastive Learning of Sentence Embeddings from Scratch](https://mlsanity.com/paper/2305.15077/) (2023)
- [On Cross-Lingual Retrieval with Multilingual Text Encoders](https://mlsanity.com/paper/2112.11031/) (2021)
- [Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework](https://mlsanity.com/paper/2210.16798/) (2022)
- [Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval](https://mlsanity.com/paper/2101.08370/) (2021)
- [Countering Online Hate Speech: An NLP Perspective](https://mlsanity.com/paper/2109.02941/) (2021)
- [MorphPiece : A Linguistic Tokenizer for Large Language Models](https://mlsanity.com/paper/2307.07262/) (2023)
- [AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters](https://mlsanity.com/paper/2108.08103/) (2021)
- [Efficient Domain Adaptation of Sentence Embeddings Using Adapters](https://mlsanity.com/paper/2307.03104/) (2023)
- [How Well Do Text Embedding Models Understand Syntax?](https://mlsanity.com/paper/2311.07996/) (2023)
- [SynWMD: Syntax-aware Word Mover's Distance for Sentence Similarity Evaluation](https://mlsanity.com/paper/2206.10029/) (2022)
- [Relational Sentence Embedding for Flexible Semantic Matching](https://mlsanity.com/paper/2212.08802/) (2022)
- [DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning](https://mlsanity.com/paper/2401.13621/) (2024)
- [Enhancing Unsupervised Sentence Embeddings via Knowledge-Driven Data Augmentation and Gaussian-Decayed Contrastive Learning](https://mlsanity.com/paper/2409.12887/) (2024)
- [Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models](https://mlsanity.com/paper/2502.13656/) (2025)
- [Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy](https://mlsanity.com/paper/2101.08201/) (2021)
- [Auto-MLM: Improved Contrastive Learning for Self-supervised Multi-lingual Knowledge Retrieval](https://mlsanity.com/paper/2203.16187/) (2022)
- [Multilingual Sentence-Level Semantic Search using Meta-Distillation Learning](https://mlsanity.com/paper/2309.08185/) (2023)
- [An Efficient Self-Supervised Cross-View Training For Sentence Embedding](https://mlsanity.com/paper/2311.03228/) (2023)
- [Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanism](https://mlsanity.com/paper/2402.12997/) (2024)
- [Improving Cross-lingual Representation for Semantic Retrieval with Code-switching](https://mlsanity.com/paper/2403.01364/) (2024)
- [Extracting the Unknown from Long Math Problems](https://mlsanity.com/paper/2103.12048/) (2021)
- [Improving Neural Ranking Models with Traditional IR Methods](https://mlsanity.com/paper/2308.15027/) (2023)
- [Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning](https://mlsanity.com/paper/2401.03563/) (2024)
- [An Approach for Auto Generation of Labeling Functions for Software Engineering Chatbots](https://mlsanity.com/paper/2410.07094/) (2024)
- [Reliable Evaluation Protocol for Low-Precision Retrieval](https://mlsanity.com/paper/2508.03306/) (2025)
- [TaxCE : A Framework for Automated Taxonomy Construction and Evaluation at Scale](https://mlsanity.com/paper/2608.30614/) (2026)
- [ZooWork-ShopRanker: An Open, Preference-Aligned E-Commerce Reranker](https://mlsanity.com/paper/2609.31002/) (2026)

## Abstract (arXiv)

Question answering forums are rapidly growing in size with no effective automated ability to refer to and reuse answers already available for previous posted questions. In this paper, we develop a methodology for finding semantically related questions. The task is difficult since 1) key pieces of information are often buried in extraneous details in the question body and 2) available annotations on similar questions are scarce and fragmented. We design a recurrent and convolutional model (gated convolution) to effectively map questions to their semantic representations. The models are pre-trained within an encoder-decoder framework (from body to title) on the basis of the entire raw corpus, and fine-tuned discriminatively from limited annotations. Our evaluation demonstrates that our model yields substantial gains over a standard IR baseline and various neural network architectures (including CNNs, LSTMs and GRUs).