# Simple and Effective Text Matching with Richer Alignment Features

arXiv: https://arxiv.org/abs/1908.00300 · HTML: https://mlsanity.com/paper/1908.00300/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2019.

## Built on by (most cited first, 29 of 29)

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- [Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching](https://mlsanity.com/paper/2101.06423/) (2021)
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- [Improving Text Semantic Similarity Modeling through a 3D Siamese Network](https://mlsanity.com/paper/2307.09274/) (2023)
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- [BERT-DRE: BERT with Deep Recursive Encoder for Natural Language Sentence Matching](https://mlsanity.com/paper/2111.02188/) (2021)
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- [K-AID: Enhancing Pre-trained Language Models with Domain Knowledge for Question Answering](https://mlsanity.com/paper/2109.10547/) (2021)
- [Description-Enhanced Label Embedding Contrastive Learning for Text Classification](https://mlsanity.com/paper/2306.08817/) (2023)
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- [MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification](https://mlsanity.com/paper/2204.04952/) (2022)
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- [A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization](https://mlsanity.com/paper/2101.09101/) (2021)
- [Semantic Matching from Different Perspectives](https://mlsanity.com/paper/2202.06517/) (2022)
- [Learning Sequence Representations by Non-local Recurrent Neural Memory](https://mlsanity.com/paper/2207.09710/) (2022)
- [KETM:A Knowledge-Enhanced Text Matching method](https://mlsanity.com/paper/2308.06235/) (2023)
- [Boosting Neural Language Inference via Cascaded Interactive Reasoning](https://mlsanity.com/paper/2505.06607/) (2025)

## Abstract (arXiv)

In this paper, we present a fast and strong neural approach for general purpose text matching applications. We explore what is sufficient to build a fast and well-performed text matching model and propose to keep three key features available for inter-sequence alignment: original point-wise features, previous aligned features, and contextual features while simplifying all the remaining components. We conduct experiments on four well-studied benchmark datasets across tasks of natural language inference, paraphrase identification and answer selection. The performance of our model is on par with the state-of-the-art on all datasets with much fewer parameters and the inference speed is at least 6 times faster compared with similarly performed ones.