Papers with autoencoder
Bag-of-Vectors Autoencoders for Unsupervised Conditional Text Generation (2022.aacl-main)
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| Challenge: | Existing methods to learn mappings in the embedding space of text autoencoders are limited to a single-vector embeddment, which limits how much information can be retained. |
| Approach: | They propose a method to learn mappings in the embedding space of an autoencoder by extending it to Bag-of-Vectors Autoencodeurs (BoV-AEs) this allows to encode and reconstruct much longer texts than standard autoencodings . |
| Outcome: | The proposed method performs better than a standard autoencoder on unsupervised sentiment transfer. |
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning (D18-1)
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| Challenge: | Existing dialog datasets rely on human labeling, which is expensive, limited in size, and in low coverage. |
| Approach: | They propose a framework to automatically cluster dialogue intents and slots . they collect context features, leverage an autoencoder for feature assembly, and adapt a dynamic hierarchical clustering method for intent and slot labeling. |
| Outcome: | The proposed framework can promote human labeling cost to a great extent and achieve good intent clustering accuracy (84.1%) it also provides reasonable and instructive slot labeling results. |
Unsupervised Energy-based Adversarial Domain Adaptation for Cross-domain Text Classification (2021.findings-acl)
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| Challenge: | Extensive experiments on multidomain sentiment classification and yes/no question-answering classification are conducted. |
| Approach: | They propose an unsupervised energy-based adversarial domain adaptation framework that maps the text sequences from both source and target domains to a feature space. |
| Outcome: | The proposed framework improves on multidomain sentiment classification and Yes/No question-answering classification. |
Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder (P18-1)
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| Challenge: | Embedding models for entities and relations are useful for recovering missing facts in knowledge bases. |
| Approach: | They propose a dimension reduction technique by training relations jointly with an autoencoder to capture compositional constraints. |
| Outcome: | The proposed model improves on Knowledge Base Completion tasks with a significantly higher mean rank and better compositional training. |
ZeroAE: Pre-trained Language Model based Autoencoder for Transductive Zero-shot Text Classification (2023.findings-acl)
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| Challenge: | Existing methods for text classification use only encoders or decoders that do not allow for the use of labels in unseen domains. |
| Approach: | They propose an autoencoder that encodes text into two disentangled spaces and decodes it to generate text with labels in the unseen domains. |
| Outcome: | The proposed model outperforms the existing methods in label-partially-unseen and label-fully-un-seeen scenarios and even outperfects the SOTA methods. |
Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder (2021.emnlp-main)
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Shuqi Lu, Di He, Chenyan Xiong, Guolin Ke, Waleed Malik, Zhicheng Dou, Paul Bennett, Tie-Yan Liu, Arnold Overwijk
| Challenge: | Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space. |
| Approach: | They propose a self-learning method that pre-trains the autoencoder using a weak decoder to push the encoder to provide better sequence representations. |
| Outcome: | The proposed model significantly boosts the effectiveness and few-shot ability of dense retrieval models on web search, news recommendation, and open domain question answering. |
Ranking-Based Autoencoder for Extreme Multi-label Classification (N19-1)
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| Challenge: | Existing methods to solve label dependency and noisy labeling problems are limited . experimental results show the proposed method is competitive to state-of-the-art methods . |
| Approach: | They propose a deep learning XML method with word-vector-based self-attention followed by ranking-based AutoEncoder architecture to solve these problems. |
| Outcome: | The proposed method is competitive to state-of-the-art methods on benchmark datasets. |
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective on Molecule Graphs (2024.findings-emnlp)
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| Challenge: | Graph Neural Networks (GNNs) are successful in molecular property prediction tasks, but their outputs are often black-box and not easily understandable by humans. |
| Approach: | They propose a method to unleash the power of large language models (LLMs) to explain GNNs for molecular property prediction. |
| Outcome: | The proposed method uses autoencoder to generate the counterfactual graph topology from a set of counterfact text pairs based on an input graph. |
Features that Make a Difference: Leveraging Gradients for Improved Dictionary Learning (2025.findings-naacl)
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| Challenge: | Sparse Autoencoders (SAEs) are a promising approach for extracting neural network representations by learning a sparse and overcomplete decomposition of the network’s internal activations. |
| Approach: | They propose a method that learns a sparse and overcomplete decomposition of the network's internal activations and a gradient approach to learn latents. |
| Outcome: | The proposed algorithms improve the performance of the k-sparse autoencoder and the ability to learn latent features. |
Learning Universal Sentence Representations with Mean-Max Attention Autoencoder (D18-1)
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| Challenge: | Existing methods to learn universal sentence representations focus on supervised learning. |
| Approach: | They propose a mean-max attention autoencoder that uses a multi-head mechanism to reconstruct the input sequence. |
| Outcome: | The proposed model outperforms state-of-the-art unsupervised single methods on a wide range of 10 transfer tasks. |
Plug and Play Autoencoders for Conditional Text Generation (2020.emnlp-main)
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| Challenge: | Text autoencoders are used for conditional generation tasks such as style transfer. |
| Approach: | They propose a plug-and-play method where any pretrained autoencoder can be used and only requires learning a mapping within the embedding space. |
| Outcome: | The proposed method performs better than or comparable to strong baselines while being up to four times faster. |
A Neural Model for Aggregating Coreference Annotation in Crowdsourcing (2020.coling-main)
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| Challenge: | Existing studies of natural language labelling tasks have shown that crowd-sourced labels can be noisy. |
| Approach: | They split the aggregation into mention classification and coreference chain inference tasks to predict the correct labels. |
| Outcome: | The proposed model predicts the class of each mention using an autoencoder while taking into account the mention’s annotation complexity and annotators’ reliability at different levels. |
Identifying Noise in Human-Created Datasets using Training Dynamics from Generative Models (2025.findings-emnlp)
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| Challenge: | Existing noise detection techniques for autoencoder models do not generalize to ArLMs due to differences in learning dynamics. |
| Approach: | They propose a method that leverages training dynamics to rank datapoints from easy-to-learn to hard-tolear . TDRanker achieves at least 2x faster denoising than previous techniques . |
| Outcome: | The proposed method demonstrates robustness across multiple model architectures and noise levels. |
Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration (2021.emnlp-main)
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| Challenge: | Phrase representations derived from pretrained language models often lack lexical similarity to determine semantic relatedness. |
| Approach: | They propose a contrastive fine-tuning objective that enables BERT to produce more powerful phrase embeddings by fine- tuning a dataset of diverse phrasal paraphrases and a large-scale dataset of phrases in context. |
| Outcome: | The proposed model outperforms baseline models across phrase-level similarity tasks while also showing increased lexical diversity between nearest neighbors in the vector space. |
LightVLP: A Lightweight Vision-Language Pre-training via Gated Interactive Masked AutoEncoders (2024.lrec-main)
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| Challenge: | Existing vision-language pre-training models use multi-modal encoders to encode image and text, causing noisy training corpora. |
| Approach: | They propose a vision-language pre-training framework with two autoencoders for efficient training . they propose masked tokens and a gated interaction mechanism to cope with noise . |
| Outcome: | The proposed model achieves 2.2% R@1 gains on COCO Text Retrieval and 1.1% on refCOCO+ on six datasets. |
Semformer: Transformer Language Models with Semantic Planning (2024.emnlp-main)
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| Challenge: | Neural language models (LLMs) employ teacher forcing to predict tokens based on preceding ground truth tokens. |
| Approach: | They propose a method for training a Transformer language model that explicitly models the semantic planning of response. |
| Outcome: | The proposed method exhibits near-perfect performance and mitigates shortcut learning. |
Debiasing Multilingual LLMs in Cross-lingual Latent Space (2025.emnlp-main)
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| Challenge: | Existing studies have evaluated their cross-lingual transferability by directly applying these methods to LLM representations, revealing their limited effectiveness across languages. |
| Approach: | They propose to perform debiasing in a joint latent space rather than directly on LLM representations by using an autoencoder trained on parallel TED talk scripts. |
| Outcome: | The proposed method improves both the overall debiasing performance and cross-lingual transferability of the proposed techniques across four languages. |
GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment (2026.acl-long)
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Jiwei Tang, Zhicheng Zhang, Shunlong Wu, Jingheng Ye, Lichen Bai, Zitai Wang, Tingwei Lu, Lin Hai, Yiming Zhao, Hai-Tao Zheng, Hong-Gee Kim
| Challenge: | Large Language Models (LLMs) have achieved remarkable performance across NLP tasks . however, in long-context scenarios, they face high computational cost and information redundancy. |
| Approach: | They propose an encoder-decoder context compression framework that generates a compact sequence of soft tokens for downstream tasks. |
| Outcome: | Experiments show that GMSA outperforms baselines on multiple long-context question answering and summarization benchmarks while maintaining low end-to-end latency. |