Papers with generative adversarial network
Deep Bayesian Natural Language Processing (P19-4)
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| Challenge: | Introduction to deep Bayesian learning for natural language addresses the fundamentals of statistical models and neural networks. |
| Approach: | This tutorial addresses the advances in deep Bayesian learning for natural language . it focuses on advanced Bayessian models and deep models . authors present case studies and domain applications to tackle different issues . |
| Outcome: | This tutorial focuses on advanced Bayesian models and deep models for natural language . case studies and domain applications are presented to tackle different issues in deep Bayessian processing, learning and understanding. |
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. |
Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection (P18-1)
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| Challenge: | Recent studies show that neural networks can be used for event detection but can be contaminated by spurious features. |
| Approach: | They propose a self-regulated learning approach by utilizing a generative adversarial network to generate spurious features. |
| Outcome: | The proposed method is highly effective and adaptable on the ACE 2005 and TAC-KBP 2015 corpora. |
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. |
An Unsupervised Framework for Adaptive Context-aware Simplified-Traditional Chinese Character Conversion (2024.lrec-main)
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| Challenge: | Traditional Chinese characters are still widely used in many areas of China . traditional methods to convert between simplified characters are ineffective . |
| Approach: | They propose an unsupervised adaptive context-aware conversion model that learns to convert between simplified and traditional Chinese characters under a denoising auto-encoder framework. |
| Outcome: | The proposed model outperforms strong unsupervised baselines and yields better conversion result for one-to-many cases. |
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 . |
G-Tuning: Improving Generalization of Pre-trained Language Models with Generative Adversarial Network (2023.findings-acl)
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| Challenge: | Empirical evaluations on the GLUE benchmark demonstrate that fine-tuning can enhance the generalization performance of pre-trained language models (PLMs) in downstream tasks. |
| Approach: | They propose a fine-tuning framework that transforms the latent representation of pre-trained language models from a universal space to a target space and integrates a generative adversarial network into the fine-untun process. |
| Outcome: | Empirical evaluations on the GLUE benchmark and two additional demanding scenarios show that the proposed framework can improve the generalization performance of pre-trained language models (PLMs) in downstream tasks. |
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. |
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%. |
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. |
A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation (2021.findings-emnlp)
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| Challenge: | Recent advances in disentanglement work on coarse levels in the disenanglement of closely related properties, such as syntax and semantics in human languages. |
| Approach: | They propose a deep decomposable model based on VAE to disentangle syntax and semantics by using total correlation penalties on KL divergences. |
| Outcome: | The proposed model significantly improves the disentanglement quality between syntactic and semantic representations for semantic similarity tasks and syntaktic similarity task. |
FastDiff 2: Revisiting and Incorporating GANs and Diffusion Models in High-Fidelity Speech Synthesis (2023.findings-acl)
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| Challenge: | Experimental results show that Generative adversarial networks sacrifice sample diversity for quality and speed, while diffusion models exhibit outperformed sample quality and diversity at a high computational cost. |
| Approach: | They propose to combine GANs and diffusion probabilistic models to achieve better sample quality and diversity. |
| Outcome: | The proposed models outperform GANs and diffusion models in speech synthesis . the proposed models enjoy an efficient 4-step sampling process and exhibit better sample diversity . |
BioFEG: Generate Latent Features for Biomedical Entity Linking (2023.emnlp-main)
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| Challenge: | Existing approaches to biomedical entity linking suffer from multiple types of errors due to the rarity of many biomedically relevant entities in real-world scenarios. |
| Approach: | They propose a latent feature generation framework to generate latent semantic features for unseen entities to capture fine-grained coherence information of unseened entities. |
| Outcome: | The proposed framework is superior to existing models on two benchmark datasets. |