# Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information

arXiv: https://arxiv.org/abs/1805.11360 · HTML: https://mlsanity.com/paper/1805.11360/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2018.

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

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- [Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents](https://mlsanity.com/paper/2203.02898/) (2022)
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- [Attention-based model for predicting question relatedness on Stack Overflow](https://mlsanity.com/paper/2103.10763/) (2021)
- [BERT-DRE: BERT with Deep Recursive Encoder for Natural Language Sentence Matching](https://mlsanity.com/paper/2111.02188/) (2021)
- [RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm](https://mlsanity.com/paper/2301.02807/) (2023)
- [You Can Do Better! If You Elaborate the Reason When Making Prediction](https://mlsanity.com/paper/2103.14919/) (2021)
- [LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching](https://mlsanity.com/paper/2108.02915/) (2021)
- [Discourse-Aware Graph Networks for Textual Logical Reasoning](https://mlsanity.com/paper/2207.01450/) (2022)
- [Description-Enhanced Label Embedding Contrastive Learning for Text Classification](https://mlsanity.com/paper/2306.08817/) (2023)
- [DEIM: An effective deep encoding and interaction model for sentence matching](https://mlsanity.com/paper/2203.10482/) (2022)
- [Persona-Aware Alignment Framework for Personalized Dialogue Generation](https://mlsanity.com/paper/2511.10215/) (2025)
- [DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching](https://mlsanity.com/paper/2106.04905/) (2021)
- [Knowledge Graph and Accurate Portrait Construction of Scientific and Technological Academic Conferences](https://mlsanity.com/paper/2204.04888/) (2022)
- [Co-Driven Recognition of Semantic Consistency via the Fusion of Transformer and HowNet Sememes Knowledge](https://mlsanity.com/paper/2302.10570/) (2023)
- [KETM:A Knowledge-Enhanced Text Matching method](https://mlsanity.com/paper/2308.06235/) (2023)
- [ETDPC: A Multimodality Framework for Classifying Pages in Electronic Theses and Dissertations](https://mlsanity.com/paper/2311.04262/) (2023)
- [A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching](https://mlsanity.com/paper/2403.02975/) (2024)

## Abstract (arXiv)

Sentence matching is widely used in various natural language tasks such as natural language inference, paraphrase identification, and question answering. For these tasks, understanding logical and semantic relationship between two sentences is required but it is yet challenging. Although attention mechanism is useful to capture the semantic relationship and to properly align the elements of two sentences, previous methods of attention mechanism simply use a summation operation which does not retain original features enough. Inspired by DenseNet, a densely connected convolutional network, we propose a densely-connected co-attentive recurrent neural network, each layer of which uses concatenated information of attentive features as well as hidden features of all the preceding recurrent layers. It enables preserving the original and the co-attentive feature information from the bottommost word embedding layer to the uppermost recurrent layer. To alleviate the problem of an ever-increasing size of feature vectors due to dense concatenation operations, we also propose to use an autoencoder after dense concatenation. We evaluate our proposed architecture on highly competitive benchmark datasets related to sentence matching. Experimental results show that our architecture, which retains recurrent and attentive features, achieves state-of-the-art performances for most of the tasks.