A Deep Network Model for Paraphrase Detection in Short Text Messages

Abstract

This paper is concerned with paraphrase detection. The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question answering. Given two sentences, the objective is to detect whether they are semantically identical. An important insight from this work is that existing paraphrase systems perform well when applied on clean texts, but they do not necessarily deliver good performance against noisy texts. Challenges with paraphrase detection on user generated short texts, such as Twitter, include language irregularity and noise. To cope with these challenges, we propose a novel deep neural network-based approach that relies on coarse-grained sentence modeling using a convolutional neural network and a long short-term memory model, combined with a specific fine-grained word-level similarity matching model. Our experimental results show that the proposed approach outperforms existing state-of-the-art approaches on user-generated noisy social media data, such as Twitter texts, and achieves highly competitive performance on a cleaner corpus.

References in corpus (10)

• ADADELTA: An Adaptive Learning Rate Method
• Natural Language Processing (almost) from Scratch
• Convolutional Neural Networks for Sentence Classification
• Character-Aware Neural Language Models
• Convolutional Neural Network Architectures for Matching Natural Language Sentences
• Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
• Sentence Similarity Learning by Lexical Decomposition and Composition
• Bilateral Multi-Perspective Matching for Natural Language Sentences
• Interpretable Semantic Textual Similarity: Finding and explaining differences between sentences
• Neural Paraphrase Identification of Questions with Noisy Pretraining

Cited by in corpus (8)

• Do Language Models Plagiarize?
• A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts
• Corpus-Based Paraphrase Detection Experiments and Review
• An enhanced Tree-LSTM architecture for sentence semantic modeling using typed dependencies
• Recognizing semantic relation in sentence pairs using Tree-RNNs and Typed dependencies
• The power of text similarity in identifying AI-LLM paraphrased documents: The case of BBC news articles and ChatGPT
• Simulation of Open Quantum Dynamics with Bootstrap-Based Long Short-Term Memory Recurrent Neural Network
• Sensitive Information Detection: Recursive Neural Networks for Encoding Context