# Syntax-based Deep Matching of Short Texts

arXiv: https://arxiv.org/abs/1503.02427 · HTML: https://mlsanity.com/paper/1503.02427/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2015.

## Built on by (most cited first, 10 of 10)

- [Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots](https://mlsanity.com/paper/2103.09534/) (2021)
- [Advances in Multi-turn Dialogue Comprehension: A Survey](https://mlsanity.com/paper/2103.03125/) (2021)
- [Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot](https://mlsanity.com/paper/2108.07935/) (2021)
- [P5: Plug-and-Play Persona Prompting for Personalized Response Selection](https://mlsanity.com/paper/2310.06390/) (2023)
- [Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain Conversation](https://mlsanity.com/paper/2108.12582/) (2021)
- [Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning](https://mlsanity.com/paper/2111.10154/) (2021)
- [Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning](https://mlsanity.com/paper/2110.00159/) (2021)
- [Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities](https://mlsanity.com/paper/2405.18542/) (2024)
- [Advances in Multi-turn Dialogue Comprehension: A Survey](https://mlsanity.com/paper/2110.04984/) (2021)
- [Learning to Detect Relevant Contexts and Knowledge for Response Selection in Retrieval-based Dialogue Systems](https://mlsanity.com/paper/2509.22845/) (2025)

## Abstract (arXiv)

Many tasks in natural language processing, ranging from machine translation to question answering, can be reduced to the problem of matching two sentences or more generally two short texts. We propose a new approach to the problem, called Deep Match Tree (DeepMatchtree), under a general setting. The approach consists of two components, 1) a mining algorithm to discover patterns for matching two short-texts, defined in the product space of dependency trees, and 2) a deep neural network for matching short texts using the mined patterns, as well as a learning algorithm to build the network having a sparse structure. We test our algorithm on the problem of matching a tweet and a response in social media, a hard matching problem proposed in [Wang et al., 2013], and show that DeepMatchtree can outperform a number of competitor models including one without using dependency trees and one based on word-embedding, all with large margins