GL-GAN: Perceiving and Integrating Global and Local Styles for Handwritten Text Generation with Mamba (2025.coling-main)
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| Challenge: | Existing models lack the ability to perceive and integrate handwriting styles, which affects the realism of the synthesized samples. |
| Approach: | They propose a Hybrid Style Encoder that captures global and local styles and integrates them into a Dynamic Feature Enhancement Module (DFEM). |
| Outcome: | The proposed model outperforms state-of-the-art models on two widely used handwriting datasets and can provide training data for handwritten text recognition and signature verification. |
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Chinese Inertial GAN for Handwriting Signal Generation and Recognition (2025.acl-long)
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| Challenge: | Inertial sensors can measure the acceleration and angular velocity of moving objects and are widely used in electronic devices such as smartphones, smartwatches, and fitness bands. |
| Approach: | They propose to use Chinese glyph encoding, forced optimal transport, and semantic relevance alignment to acquire unlimited training samples for Chinese inertial writing recognition. |
| Outcome: | The proposed system improves the performance of six widely used classifiers from 6.7% to 98.4%. |
SenDetEX: Sentence-Level AI-Generated Text Detection for Human-AI Hybrid Content via Style and Context Fusion (2025.emnlp-main)
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| Challenge: | Text generated by Large Language Models (LLMs) now rivals human writing, raising concerns about its misuse. |
| Approach: | They propose a framework for sentence-level AI-generated text detection via style and context fusion. |
| Outcome: | The proposed framework outperforms baseline models in detection accuracy while exhibiting transferability and robustness. |
Detecting, Generating, and Evaluating in the Writing Style of Different Authors (2025.naacl-srw)
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| Challenge: | In recent years, stylometry has been investigated in many different fields. |
| Approach: | They propose to use sentences from different books to generate and evaluate stylistic texts according to the authors' writing styles. |
| Outcome: | The proposed model can detect, generate, and evaluate documents according to the authors' writing styles with unpaired samples. |
HS-GC: Holistic Semantic Embedding and Global Contrast for Effective Text Clustering (2024.lrec-main)
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| Challenge: | In this paper, we introduce Holistic Semantic Embedding and Global Contrast (HS-GC) to learn the instance- and cluster-level representations. |
| Approach: | They propose a novel loss function that exploits different layers of semantic information in a deep neural network to provide a more holistic semantic text representation. |
| Outcome: | The proposed model outperforms the state-of-the-art model on five text datasets and improves clustering accuracy of 5.9% and 3.2% on the StackOverflow and TREC datasets. |
Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models (2024.lrec-main)
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| Challenge: | Existing methods for autoregressive text generation have low controllability and accumulating errors. |
| Approach: | They propose a three-stage prompt-based approach to express autoregressive text in a specific region editing task using a word frequency-based strategy. |
| Outcome: | Experiments on publicly competitive datasets confirm that the proposed approach achieves state-of-the-art performance. |
Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models (2022.acl-long)
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| Challenge: | Recent work on controlled text generation has required attribute-based fine-tuning of the base language model or restricted the parameterization of the attribute discriminator. |
| Approach: | They propose a global score-based alternative for controllable text generation that combines arbitrary pre-trained black-box models for achieving desired attributes in the generated text. |
| Outcome: | The proposed method outperforms methods that require extra training or fine-tuning . the proposed method is based on a model with energy values of a linear combination of scores from black-box models . |
Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training (D19-1)
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| Challenge: | Generative adversarial network (GAN) is a popular model for text style transfer . but, training GAN often suffers from mode collapse problem, which causes that the transferred text is little related to the original text. |
| Approach: | They propose a non-parallel text style transfer model with a word-level conditional architecture and a two-phase training procedure to maintain style-unrelated words while changing others. |
| Outcome: | The proposed model outperforms state-of-the-art models on three real-world datasets in transfer accuracy and fluency. |
A Deep Factorization of Style and Structure in Fonts (D19-1)
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| Challenge: | Using a variational inference procedure, we factor each training glyph into a combination of a character-specific content embedding and a latent font-specific style variable. |
| Approach: | They propose a deep factorization model that disentangles content from style by factorizing each training glyph into a latent content embedding and a learned embeddable character. |
| Outcome: | The proposed model outperforms a strong nearest neighbors baseline and state-of-the-art discriminative model on reconstructing missing glyphs from an unknown font given only a small number of observations. |
PlagBench: Exploring the Duality of Large Language Models in Plagiarism Generation and Detection (2025.naacl-long)
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| Challenge: | Recent studies have raised concerns about the potential threats large language models pose to academic integrity and copyright protection. |
| Approach: | They propose a dataset of 46.5K synthetic text pairs that represent three major types of plagiarism: verbatim copying, paraphrasing, and summarization. |
| Outcome: | The proposed dataset shows that GPT-3.5 Turbo can produce high-quality paraphrases and summaries without significantly increasing text complexity compared to GPT-4 Turbo. |
EDU-CIRCUIT-HW: Evaluating Multimodal Large Language Models on Real-World University-Level STEM Student Handwritten Solutions (2026.findings-acl)
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| Challenge: | Multimodal Large Language Models (MLLMs) are a promising tool for traditional education but lack authentic and domain-specific benchmarks to accurately interpret student handwritten solutions. |
| Approach: | They propose to use MLLMs to interpret unconstrained STEM student handwritten solutions with intertwined mathematical formulas, diagrams, and textual reasoning to bridge this gap. |
| Outcome: | The proposed model can detect and rectify recognition errors with minimal human intervention on unseen student solutions. |