Papers with GAN
TextBox: A Unified, Modularized, and Extensible Framework for Text Generation (2021.acl-demo)
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Junyi Li, Tianyi Tang, Gaole He, Jinhao Jiang, Xiaoxuan Hu, Puzhao Xie, Zhipeng Chen, Zhuohao Yu, Wayne Xin Zhao, Ji-Rong Wen
| Challenge: | TextBox is an open-source text generation framework that is modularized and extensible. |
| Approach: | They propose to provide a unified, modularized, and extensible text generation framework that implements 21 text generation models on 9 benchmark datasets. |
| Outcome: | The proposed framework implements 21 models on 9 benchmark datasets and is available under the Apache License 2.0 license. |
Unsupervised Neural Machine Translation with Weight Sharing (P18-1)
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| Challenge: | Unsupervised neural machine translation (NMT) is a new approach for machine translation . the model uses only one shared encoder to map pairs of sentences from different languages to a shared-latent space . |
| Approach: | They propose an unsupervised approach which trains the model without labeling data . they propose two independent encoders but share some partial weights to extract high-level representations of input sentences. |
| Outcome: | The proposed approach achieves significant improvements on English-German, English-French and Chinese-to-English translation tasks. |
MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data (2026.findings-acl)
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| Challenge: | Existing models for tabular data generation require large amounts of data to train effectively. |
| Approach: | They propose a framework to generate tabular data powered by large language models that emulates a Generative Adversarial Network. |
| Outcome: | The proposed framework outperforms state-of-the-art models while keeping privacy of real data. |
GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization (2025.acl-long)
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Zhouhong Gu, Xingzhou Chen, Xiaoran Shi, Tao Wang, Suhang Zheng, Tianyu Li, Hongwei Feng, Yanghua Xiao
| Challenge: | Existing methods for achieving this require a limited understanding of constraints and can be hallucinating or brittle. |
| Approach: | They propose a framework that combines adversarial training dynamics with an encoder-only reward model to progressively learn and adapt to increasingly complex constraints. |
| Outcome: | Extensive experiments show that GAPO significantly outperforms existing methods like PPO, DPO, and KTO in fine-grained constraints. |
Answer-based Adversarial Training for Generating Clarification Questions (N19-1)
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| Challenge: | a goal of natural language processing is to develop techniques that enable machines to process naturally occurring language. |
| Approach: | They propose a model where hypothetical answers are latent variables that can guide the model into generating more useful clarification questions. |
| Outcome: | The proposed model outperforms retrieval-based models and ablations that exclude utility model and adversarial training on two datasets. |
P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Models (2024.findings-acl)
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| Challenge: | Contemporary approaches to generate tabular data are limited due to the lack of external knowledge. |
| Approach: | They propose to use proximal policy optimization to apply GANs and fine-tune Large Language Models to enhance the probability distribution of tabular features. |
| Outcome: | The proposed method improves accuracy of GANs and LLMs over state-of-the-art over three real-world datasets. |
Making Use of Latent Space in Language GANs for Generating Diverse Text without Pre-training (2021.eacl-srw)
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| Challenge: | Existing models for generating diverse texts are not pre-trained . generative adversarial networks suffer from mode-collapsing if they are not trained . |
| Approach: | They propose a GAN model that produces diverse texts conditioned by latent code . they propose to use Gumbel-Softmax distribution for word sampling . |
| Outcome: | The proposed model is competitive with existing models, which requires pre-training. |
Forged-GAN-BERT: Authorship Attribution for LLM-Generated Forged Novels (2024.eacl-srw)
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| Challenge: | generative Large Language Models (LLMs) are capable of producing human-like texts, but they pose challenges related to the authenticity of the text documents. |
| Approach: | They propose a modified GANBERT-based model to improve the classification of forged novels via the Forged Novels Generator and the generator in GAN. |
| Outcome: | The proposed model improves classification of forged novels in two data-augmentation aspects. |
OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation (2021.naacl-industry)
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| Challenge: | Existing models for OOD detection work with text, but they do not work directly with the text. |
| Approach: | They propose to use a sequential generative adversarial network (SeqGAN) based model to generate OOD data for a given domain automatically. |
| Outcome: | The proposed model outperforms state-of-the-art in OOD detection metrics for ROSTD and OSQ datasets. |
So Different Yet So Alike! Constrained Unsupervised Text Style Transfer (2022.acl-long)
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| Challenge: | Automated transfer of text between domains does not maintain other attributes between the source and translated text. |
| Approach: | They propose a method for automatic transfer of text between domains that preserves semantic content but changes other attributes. |
| Outcome: | The proposed method retains lexical, syntactic and domain-specific constraints between domains for multiple benchmark datasets, including ones where more than one attribute change. |
Adversarial Feature Adaptation for Cross-lingual Relation Classification (C18-1)
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| Challenge: | Existing methods for relation classification exploit monolingual data due to lack of annotated data in other languages. |
| Approach: | They propose an adversarial feature adaptation approach for cross-lingual relation classification using a generative adversarial network. |
| Outcome: | The proposed approach yields an improvement of 5.7% over the state-of-the-art. |
Authorship Attribution of Late 19th Century Novels using GAN-BERT (2023.acl-srw)
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| Challenge: | Conventional techniques and neural networks are the two main authorship attribution methods. |
| Approach: | They used a dataset of late 19th century novels in English to fine-tune a transformer-based authorship attribution model using transfer learning. |
| Outcome: | The proposed model outperforms the existing model with 0.88 accuracy and F1 scores. |
Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation (2025.emnlp-main)
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| Challenge: | Intrusion detection systems (IDS) are limited in labeled samples due to scarcity and lack of diversity in malicious samples. |
| Approach: | They propose a semi-supervised framework that integrates Generative Adversarial Networks with Large Language Models to enhance malicious code generation and SQL Injection detection capabilities. |
| Outcome: | The proposed framework enhances malicious code generation and detection capabilities in few-sample learning scenarios. |
Out-of-domain Detection based on Generative Adversarial Network (D18-1)
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| Challenge: | Existing methods for out-of-domain (OOD) detection require huge effort to collect OOD sentences. |
| Approach: | They propose to use only in-domain (IND) sentences to build a generative adversarial network (GAN) of which the discriminator generates low scores for OOD sentences. |
| Outcome: | The proposed method is most accurate compared to existing methods on multi-domain dialog systems. |
Learning to Collaborate for Question Answering and Asking (N18-1)
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| Challenge: | Question answering (QA) and question generation (QG) are closely related tasks. |
| Approach: | They propose a training algorithm that generalizes both Generative Adversarial Network and Generating Domain-Adaptive Nets under the question answering scenario. |
| Outcome: | The proposed training algorithm generalizes both Generative Adversarial Network (GAN) and Generating Domain-Adaptive Nets (GDAN) under the question answering scenario. |
ToxiGAN: Toxic Data Augmentation via LLM-Guided Directional Adversarial Generation (2026.eacl-long)
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| Challenge: | Generating toxic text for augmentation is a sensitive and challenging task . toxic language remains pervasive, often escaping automated moderation systems . |
| Approach: | They propose a class-aware text augmentation framework that combines adversarial generation with semantic guidance from large language models to provide balanced guidance. |
| Outcome: | Experiments on four hate speech benchmarks show that ToxiGAN outperforms traditional and LLM-based augmentation methods. |
“Barking up the Right Tree”, a GAN-Based Pun Generation Model through Semantic Pruning (2024.lrec-main)
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| Challenge: | Existing methods for generating humorous puns are limited and require a broad spectrum of commonsense and worldly skills. |
| Approach: | They propose a GAN-based approach that employs semantic pruning and contrastive learning to generate humorous puns using a model that captures the semantic nuances of puns. |
| Outcome: | The proposed model produces semantically coherent and humorous puns while ensuring both correctness and humor. |
GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples (2020.acl-main)
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| Challenge: | Recent Transformer-based architectures provide impressive results in many NLP tasks, but obtaining high-quality annotated data is expensive and time consuming. |
| Approach: | They propose a semisupervised learning method that ex- tends the fine-tuning of BERT-like architectures with unlabeled data in a generative adversarial setting. |
| Outcome: | The proposed method reduces the requirement for annotated examples while achieving good performance in sentence classification tasks. |
AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples (P18-1)
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| Challenge: | Recent deep learning entailment systems have achieved close to human level performance on large datasets, but the problem is far from solved. |
| Approach: | They propose a knowledge-guided adversarial example generator for incorporating large lexical resources into entailment models via only a handful of rule templates and a natural language example generator that iteratively adjusts to the discriminator’s weaknesses. |
| Outcome: | The proposed methods increase accuracy by 4.7% on SciTail and 2.8% on a 1% sub-sample of SNLI. |
Evaluating Text GANs as Language Models (N19-1)
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| Challenge: | Generative Adversarial Networks (GANs) do not suffer from the problem of exposure bias. |
| Approach: | They propose to approximate the distribution of text generated by a GAN and compare it to traditional probability-based LM metrics. |
| Outcome: | The proposed method performs significantly worse than state-of-the-art LMs on several GAN-based models and can accelerate progress in GAN text generation. |
Finding Influential Instances for Distantly Supervised Relation Extraction (2022.coling-1)
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| Challenge: | Distant supervision models suffer from high label noise and are not reliable for DS. |
| Approach: | They propose a model-agnostic instance sampling method for relation extraction (RE) by influence function, namely REIF. |
| Outcome: | The proposed method reduces the computational complexity from O(mn) to O(1), with analyzing its robustness on the selected sampling function. |
Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation (N19-1)
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| Challenge: | Text generation with generative adversarial networks (GANs) can be divided into text-based and code-based categories depending on the type of signals used for discrimination. |
| Approach: | They propose a text-based approach to exploit generative adversarial networks (GANs) by using autoencoders to provide a continuous representation of sentences, which they will refer to as soft-text, and hybrid latent code and text-oriented approaches with one or more discriminators. |
| Outcome: | The proposed approach outperforms the traditional GAN-based methods on two well-known datasets. |
Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training (2020.emnlp-main)
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| Challenge: | Existing approaches to task-oriented dialogue systems require a large number of handcrafted features and labels. |
| Approach: | They propose a "Two-Teacher One-Student" learning framework for task-oriented dialogue . the framework amalgamates knowledge from two teacher networks and provides guidance . |
| Outcome: | The proposed framework outperforms baseline methods on two benchmark datasets . it can retrieve accurate KB entities and generate human-like responses simultaneously . |
Grammatical Error Correction as GAN-like Sequence Labeling (2021.findings-acl)
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| Challenge: | Traditional GEC models learn from sentences with fixed error rates . sequence labeling approaches suffer from a couple of key problems . |
| Approach: | They propose a GAN-like sequence labeling model with a grammatical error detector and a generator to correct grammamatical errors. |
| Outcome: | The proposed model improves the state-of-the-art in GEC and improves on benchmarks. |
Semi-Supervised Disfluency Detection (C18-1)
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| Challenge: | Detecting disfluency can be difficult because of the flexible nature of reparandum structure and the lack of a nested structure. |
| Approach: | They propose a semi-supervised approach which extracts hidden features from self-attention without any Recurrent Neural Network (RNN) or Convolutional Neural Net (CNN). |
| Outcome: | The proposed approach improves over baselines by using unlabelled data . identifying and removing non-fluent factors would help to improve spontaneous speech quality . |
Generating Realistic Natural Language Counterfactuals (2021.findings-emnlp)
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| Challenge: | Existing methods to explain ML tasks for natural language text are either unrealistic or introduce imperceptible changes. |
| Approach: | They propose a method that combines a conditional GAN and embeddings of a pretrained BERT encoder to model-agnostically generate realistic natural language text counterfactuals for explaining regression and classification tasks. |
| Outcome: | The proposed method outperforms baseline methods on fidelity and human judgments of naturalness across multiple datasets and multiple predictive models. |
GAN Driven Semi-distant Supervision for Relation Extraction (N19-1)
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| Challenge: | Existing methods for relation extraction are limited to costly hand-labeled training sets and hard to be extended to large-scale relations. |
| Approach: | They propose a semi-distant supervision approach for relation extraction by constructing a small accurate dataset and properly leveraging numerous instances without relation labels. |
| Outcome: | The proposed approach achieves significant improvements over baselines on real-world datasets. |
Autoregressive Text Generation Beyond Feedback Loops (D19-1)
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| Challenge: | Autoregressive feedback exposes the evolution of the hidden state trajectory to potential biases from well-known train-test discrepancies. |
| Approach: | They combine a latent state space model with a CRF observation model to investigate the state evolution of a hidden state trajectory. |
| Outcome: | The proposed model performs better on unconditional sentence generation compared to baselines while avoiding some prototypical failure modes. |
CAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks on their Internal Representations (2025.findings-acl)
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| Challenge: | Existing studies have isolated LLM jailbreak attacks and defenses . a new framework combines attack and defense to protect against malicious queries . |
| Approach: | They propose a framework that combines attack and defense to protect the Large Language Model (LLM) by embedding harmful problems into the safe area. |
| Outcome: | The proposed framework achieves an average jailbreak success rate of 88.85% across three popular LLMs while the defense success rate reaches an average of 84.17%. |
Learning to Model Multimodal Semantic Alignment for Story Visualization (2022.findings-emnlp)
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| Challenge: | Story visualization aims to generate sequence of images to narrate each sentence in a multi-sentence story . current methods face semantic misalignment because of their fixed architecture and diversity of input modalities . |
| Approach: | They propose to use a GAN-based generative model to match semantic levels between text and image representations to solve the semantic misalignment problem. |
| Outcome: | Experiments show that the proposed approach improves image quality and story consistency compared with state-of-the-art methods. |
Continuous Language Generative Flow (2021.acl-long)
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| Challenge: | Recent years have witnessed various types of generative models for natural language generation (NLG), especially RNNs or transformers. |
| Approach: | They propose a flow-based language generation model that adapts flow-derived generative models to language generation via continuous input embeddings, adapted affine coupling structures, and a novel architecture for autoregressive text generation. |
| Outcome: | The proposed model improves on QG and NMT and improves performance over baselines on SQuAD and TVQA and NML16. |
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. |
DAdEE: Unsupervised Domain Adaptation in Early Exit PLMs (2024.findings-emnlp)
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| Challenge: | Pre-trained Language Models (PLMs) exhibit good accuracy and generalization ability but their large size results in high inference latency. |
| Approach: | They propose an unsupervised domain adaptation framework that employs knowledge distillation to achieve domain-invariant representations at each layer. |
| Outcome: | The proposed framework outperforms early exit methods and domain adaptation methods under domain shift scenarios. |
GraDA: Graph Generative Data Augmentation for Commonsense Reasoning (2022.coling-1)
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| Challenge: | Recent advances in commonsense reasoning have been fueled by the availability of large-scale human annotated datasets. |
| Approach: | They propose a graph-generative data augmentation framework to synthesize factual data samples from knowledge graphs for commonsense reasoning. |
| Outcome: | The proposed framework improves SocialIQA, CODAH, HellaSwag and CommonsenseQA . it also performs well for generative tasks like ProtoQA proving its robustness to adversaries . |
TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation (2021.findings-acl)
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| Challenge: | Existing autoregressive models suffer from the exposure bias problem due to mismatches between training and generation stages. |
| Approach: | They propose a Transformerbased Implicit Latent GAN which combines a transformer autoencoder and GAN in the latent space with a novel design and distribution matching based on the Kullback-Leibler divergence. |
| Outcome: | The proposed model improves local and global coherence and quality-diversity trade-off on three benchmark datasets. |
ARAML: A Stable Adversarial Training Framework for Text Generation (D19-1)
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| Challenge: | Existing generative adversarial networks suffer from the instability of reinforcement learning training algorithms such as policy gradient, leading to unstable performance. |
| Approach: | They propose a framework where the discriminator assigns rewards to samples acquired from a stationary distribution near the data rather than the generator’s distribution. |
| Outcome: | The proposed framework outperforms state-of-the-art text GANs with a more stable training process. |
CoCGAN: Contrastive Learning for Adversarial Category Text Generation (2022.coling-1)
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| Challenge: | Experimental results on synthetic and real category text generation datasets demonstrate that CoCGAN can achieve significant improvements over the baseline category text generators. |
| Approach: | They propose to incorporate contrastive learning into adversarial category text generation by using a discriminator to optimize a contrastive learn objective to capture more flexible data-to-class relations and data- to-data relations among training samples. |
| Outcome: | The proposed model improves on synthetic and real category text generation datasets. |
Revisiting Over-Smoothness in Text to Speech (2022.acl-long)
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| Challenge: | Non-autoregressive text to speech models ignore correlation in time and frequency domains, causing blurry results. |
| Approach: | They revisit the problem of over-smoothness in non-autoregressive text to speech models . they use methods that reduce complexity of data distributions and improve modeling methods . |
| Outcome: | The proposed models achieve better voice quality and faster inference speed than autoregressive models. |
Best Student Forcing: A Simple Training Mechanism in Adversarial Language Generation (2020.lrec-1)
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| Challenge: | Language models trained with Maximum Likelihood Estimation (MLE) have been considered as a mainstream solution in Natural Language Generation (NLG) however, they are reportedly suffering from training instability and mode collapse, and therefore outperform conventional MLE models. |
| Approach: | They propose a method to improve Generative Adversarial Nets (GANs) using best student forcing and discriminators to increase training stability and sample diversity. |
| Outcome: | The proposed techniques outperform MLE models and outperformed existing approaches in terms of sample diversity and training stability. |
Text-Free Prosody-Aware Generative Spoken Language Modeling (2022.acl-long)
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Eugene Kharitonov, Ann Lee, Adam Polyak, Yossi Adi, Jade Copet, Kushal Lakhotia, Tu Anh Nguyen, Morgane Riviere, Abdelrahman Mohamed, Emmanuel Dupoux, Wei-Ning Hsu
| Challenge: | Experimental results show that generative spoken language models (LMs) are natural unsupervised multitask learners. |
| Approach: | They propose a prosody-aware generative spoken language model that uses discovered units to generate natural, meaningful, and coherent speech. |
| Outcome: | The proposed model can generate natural, meaningful, and coherent speech given a spoken prompt. |
Topic Modeling with Wasserstein Autoencoders (P19-1)
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| Challenge: | Existing probabilistic topic models are based on latent Dirichlet allocations and collapsed Gibbs sampling. |
| Approach: | They propose a novel topic model that enforces Dirichlet prior on latent document-topic vectors and a kernel kernel to minimize the Maximum Mean Discrepancy (MMD) They propose to measure the diversity of the produced topics and to use the widely used coherence measure NPMI to evaluate topic quality. |
| Outcome: | The proposed model performs better than existing topic models on real datasets. |
Hierarchical Topic Modeling via Contrastive Learning and Hyperbolic Embedding (2024.lrec-main)
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| Challenge: | Existing hierarchical topic models are based on Euclidean space, which cannot retain the hierarchically semantic information in the corpus, leading to irrational structure of the generated topics. |
| Approach: | They propose a novel hierarchical topic model that uses contrastive learning to capture information from documents. |
| Outcome: | The proposed model performs on topic coherence and topic diversity, and on the rationality of the topic hierarchy. |
A Structure-Aware Generative Adversarial Network for Bilingual Lexicon Induction (2023.findings-emnlp)
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| Challenge: | Bilingual lexicon induction (BLI) is the task of inducing word translations with a learned mapping function that aligns monolingual word embedding spaces in two different languages. |
| Approach: | They propose a model that explicitly captures multiple topological structure information to achieve accurate bilingual lexicon induction. |
| Outcome: | The proposed model captures multiple topological structure information to achieve accurate BLI on a public dataset. |
Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion (2021.emnlp-main)
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| Challenge: | Existing methods for entity set expansion define the expansion boundary using seed-based distance metrics, which are hard to adjust due to the extremely sparse supervision. |
| Approach: | They propose a new learning method for bootstrapping which jointly models the bootstraping process and boundary learning process in a GAN framework. |
| Outcome: | The proposed method achieves the new state-of-the-art performance for entity set expansion. |
FREE: Fast and Robust Vision Language Models with Early Exits (2025.findings-acl)
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| Challenge: | Vision-Language Models (VLMs) have shown remarkable performance improvements in Vision-language tasks, but their large size poses challenges for real-world applications. |
| Approach: | They propose an adversarial approach to train exit classifiers in Vision-Language Models that uses a transformer layer and a classifier to perform input-adaptive inference. |
| Outcome: | The proposed approach speeds up inference speed with minimal drop in performance by 1.51 while retaining comparable performance. |
Releasing the Capacity of GANs in Non-Autoregressive Image Captioning (2024.lrec-main)
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| Challenge: | Existing non-autoregressive (NAR) models suffer from their inherent multi-modality problem. |
| Approach: | They propose an Adversarial Non-autoregressive Transformer for Image Captioning that improves model performance by modifying model structure to be compatible with contrastive learning. |
| Outcome: | The proposed model achieves 26.72 times faster than the autoregressive model on the MSCOCO dataset. |
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%. |