# REGMAPR - Text Matching Made Easy

arXiv: https://arxiv.org/abs/1808.04343 · HTML: https://mlsanity.com/paper/1808.04343/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2018.

## Built on by (most cited first, 2 of 2)

- [Contextualized Embeddings based Convolutional Neural Networks for Duplicate Question Identification](https://mlsanity.com/paper/2109.01560/) (2021)
- [R-DEIM Net: An Efficient Rationale-Augmented Dual-Expert Interaction Model for Paraphrase Detection](https://mlsanity.com/paper/2609.30100/) (2026)

## Abstract (arXiv)

Text matching is a fundamental problem in natural language processing. Neural models using bidirectional LSTMs for sentence encoding and inter-sentence attention mechanisms perform remarkably well on several benchmark datasets. We propose REGMAPR - a simple and general architecture for text matching that does not use inter-sentence attention. Starting from a Siamese architecture, we augment the embeddings of the words with two features based on exact and para- phrase match between words in the two sentences. We train the model using three types of regularization on datasets for textual entailment, paraphrase detection and semantic related- ness. REGMAPR performs comparably or better than more complex neural models or models using a large number of handcrafted features. REGMAPR achieves state-of-the-art results for paraphrase detection on the SICK dataset and for textual entailment on the SNLI dataset among models that do not use inter-sentence attention.