# Context Enhanced Short Text Matching using Clickthrough Data

arXiv: https://arxiv.org/abs/2203.01849 · HTML: https://mlsanity.com/paper/2203.01849/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://mlsanity.com/paper/2107.02137/) (2021)
- [Bilateral Multi-Perspective Matching for Natural Language Sentences](https://mlsanity.com/paper/1702.03814/) (2017)
- [Learning Natural Language Inference with LSTM](https://mlsanity.com/paper/1512.08849/) (2015)
- [Glyce: Glyph-vectors for Chinese Character Representations](https://mlsanity.com/paper/1901.10125/) (2019)
- [ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders](https://mlsanity.com/paper/2105.01279/) (2021)
- [LET: Linguistic Knowledge Enhanced Graph Transformer for Chinese Short Text Matching](https://mlsanity.com/paper/2102.12671/) (2021)
- [Lattice CNNs for Matching Based Chinese Question Answering](https://mlsanity.com/paper/1902.09087/) (2019)

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

- [The Short Text Matching Model Enhanced with Knowledge via Contrastive Learning](https://mlsanity.com/paper/2304.03898/) (2023)

## Abstract (arXiv)

The short text matching task employs a model to determine whether two short texts have the same semantic meaning or intent. Existing short text matching models usually rely on the content of short texts which are lack information or missing some key clues. Therefore, the short texts need external knowledge to complete their semantic meaning. To address this issue, we propose a new short text matching framework for introducing external knowledge to enhance the short text contextual representation. In detail, we apply a self-attention mechanism to enrich short text representation with external contexts. Experiments on two Chinese datasets and one English dataset demonstrate that our framework outperforms the state-of-the-art short text matching models.