# Chinese Sentences Similarity via Cross-Attention Based Siamese Network

arXiv: https://arxiv.org/abs/2104.08787 · HTML: https://mlsanity.com/paper/2104.08787/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Natural Language Inference by Tree-Based Convolution and Heuristic Matching](https://mlsanity.com/paper/1512.08422/) (2015)
- [Predicting the Semantic Textual Similarity with Siamese CNN and LSTM](https://mlsanity.com/paper/1810.10641/) (2018)
- [Sentence Embeddings in NLI with Iterative Refinement Encoders](https://mlsanity.com/paper/1808.08762/) (2018)

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

- [MultiSiam: A Multiple Input Siamese Network For Social Media Text Classification And Duplicate Text Detection](https://mlsanity.com/paper/2401.06783/) (2024)

## Abstract (arXiv)

Measuring sentence similarity is a key research area nowadays as it allows machines to better understand human languages. In this paper, we proposed a Cross-Attention Siamese Network (CATsNet) to carry out the task of learning the semantic meanings of Chinese sentences and comparing the similarity between two sentences. This novel model is capable of catching non-local features. Additionally, we also tried to apply the long short-term memory (LSTM) network in the model to improve its performance. The experiments were conducted on the LCQMC dataset and the results showed that our model could achieve a higher accuracy than previous work.