Papers with unsupervised baselines
MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languages (2024.naacl-short)
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| Challenge: | Text detoxification is a textual style transfer task where a toxic text is paraphrased to the neutral register. |
| Approach: | They propose to extend ParaDetox pipeline to multiple languages to automate parallel detoxification corpus collection. |
| Outcome: | The proposed methods have been used in toxic speech combating and toxic speech fighting tasks. |
Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes (2021.tacl-1)
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| Challenge: | Recent studies have focused on training complex neural networks on labeled data. |
| Approach: | They propose to use logical mechanisms to classify argumentative relations without training on labeled data. |
| Outcome: | The proposed method classifies argumentative relations without training on labeled data significantly better than unsupervised baselines. |
A Discriminative Neural Model for Cross-Lingual Word Alignment (D19-1)
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| Challenge: | a novel word alignment model for machine translation has been developed for a number of languages . explicit word-to-word alignments have largely been lost in neural MT systems . |
| Approach: | They propose a discriminative word alignment model which integrates into a Transformer-based machine translation model. |
| Outcome: | The proposed model performs better on Chinese and Arabic alignments than standard models. |
Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages (2021.naacl-main)
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| Challenge: | Unsupervised translation systems have impressive performance on resource-rich language pairs . however, in more realistic settings, unsupervised systems perform poorly . |
| Approach: | They propose a model for 5 low-resource languages that leverages monolingual and auxiliary parallel data from other high-resourced languages. |
| Outcome: | The proposed model outperforms state-of-the-art models on low-resource languages . it also matches the current state- of-the art model for Nepali-English . |
Discourse-Aware Unsupervised Summarization for Long Scientific Documents (2021.eacl-main)
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| Challenge: | Existing extractive models for short news summarization are weak, despite recent advances in abstractive summarizing. |
| Approach: | They propose an unsupervised graph-based ranking model that uses a hierarchical graph representation to determine sentence importance. |
| Outcome: | The proposed model outperforms strong unsupervised baselines by wide margins in automatic metrics and human evaluation. |
Learning Disentangled Representations for Natural Language Definitions (2023.findings-eacl)
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| Challenge: | Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control and downstream task performance in Natural Language Processing. |
| Approach: | They propose to use syntactic and semantic regularities in textual data to provide models with both structural biases and generative factors. |
| Outcome: | The proposed model outperforms baselines on several qualitative and quantitative benchmarks and improves the results in the downstream task of definition modeling. |
Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models (2021.acl-long)
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| Challenge: | Existing methods for generating text are unsupervised and require supervision. |
| Approach: | They propose an unsupervised method that uses two off-the-shelf pretrained LMs in opposite directions to apply them to non-sequential tasks. |
| Outcome: | The proposed method outperforms strong unsupervised baselines on paraphrasing and abductive text infilling. |
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. |
Unsupervised Natural Language Inference Using PHL Triplet Generation (2022.findings-acl)
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| Challenge: | In some cases, training samples may not be available or collecting them could be time-consuming and resource-intensive. |
| Approach: | They propose a procedural approach that leverages sentence transformations to collect PHL triplets for training NLI models. |
| Outcome: | The proposed model outperforms existing models on several NLI benchmarks with a set of sentence transformations. |
StrucSum: Graph-Structured Reasoning for Long Document Extractive Summarization with LLMs (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) have shown strong performance in zero-shot summarization, but struggle to model document structure and identify salient information in long texts. |
| Approach: | They propose a training-free prompting framework that injects structural signals into prompts via sentence-level graph structures. |
| Outcome: | The proposed framework improves summary quality and factual consistency over baselines and vanilla prompting. |
BaseCal: Unsupervised Confidence Calibration via Base Model Signals (2026.acl-long)
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Hexiang Tan, Wanli Yang, Junwei Zhang, Xin Chen, Rui Tang, Du Su, Jingang Wang, Yuanzhuo Wang, Fei Sun, Xueqi Cheng
| Challenge: | Post-trained LLMs typically compromise reliability with severe overconfidence, resulting in inaccurate responses. |
| Approach: | They propose a solution that feeds PoLLMs into the base LLM to get confidence. |
| Outcome: | The proposed solution reduces expected calibration error (ECE) by 42.90% compared to the best unsupervised baselines. |
Contrastive Learning of Sentence Embeddings from Scratch (2023.emnlp-main)
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| Challenge: | Existing approaches to learn sentence embeddings with unlabeled data are limited due to copyright restrictions, data distribution issues, and messy formats. |
| Approach: | They propose a contrastive learning framework that trains sentence embeddings with synthetic data. |
| Outcome: | The proposed framework produces positive and negative annotations given unlabeled sentences and generates sentences along with their corresponding annotations from scratch. |
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle (D19-1)
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| Challenge: | Existing approaches to extractive and abstractive summarization rely on large-scale parallel corpora of input text and output summaries for direct supervision. |
| Approach: | They propose an unsupervised approach to sentence summarization using the Information Bottleneck principle. |
| Outcome: | The proposed method outperforms unsupervised models on automatic metrics and human evaluation along multiple attributes. |
MCPG: A Flexible Multi-Level Controllable Framework for Unsupervised Paraphrase Generation (2022.findings-emnlp)
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| Challenge: | Existing studies on controllable unsupervised paraphrase generation are expensive and require supervised training on large parallel corpora. |
| Approach: | They propose a method for controllable unsupervised paraphrase generation that is flexible to adapt to specific domains without extra training. |
| Outcome: | The proposed method outperforms state-of-the-art unsupervised baselines by a margin. |
Automatically Generated Definitions and their utility for Modeling Word Meaning (2024.emnlp-main)
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| Challenge: | Modern language models generate semantic representations for words based on context and context based models. |
| Approach: | They propose to use dictionary-like sense definitions to generate sentence embeddings . they evaluate the quality of the generated definitions on existing English benchmarks based on the results of their study . |
| Outcome: | The proposed model sets new state-of-the-art results on lexical semantics tasks compared to baselines . |
Label Confidence Weighted Learning for Target-level Sentence Simplification (2024.emnlp-main)
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| Challenge: | Existing methods for sentence simplification use label confidence weighting to generate pseudo-labeled sentences with varying proficiency levels. |
| Approach: | They propose a label confidence weighting scheme for multi-level sentence simplification that incorporates a weighting system into the training loss of the encoder-decoder model. |
| Outcome: | The proposed approach outperforms state-of-the-art confidence weighting methods on English grade-level simplification datasets. |
Paired by the Teacher: Turning Unpaired Data into High-Fidelity Pairs for Low-Resource Text Generation (2025.emnlp-main)
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| Challenge: | a low-resource natural language generation task requires a large number of examples to generate outputs and outputs. |
| Approach: | They propose a teacher-student pipeline that synthesizes accurate input–output pairs without human labels or parallel data. |
| Outcome: | The proposed pipeline synthesizes accurate input–output pairs without human labels or parallel data. |
ERU-KG: Efficient Reference-aligned Unsupervised Keyphrase Generation (2025.acl-long)
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| Challenge: | Existing methods for keyphrase prediction rely on heuristicically defined importance scores . existing methods lack consideration for time efficiency . |
| Approach: | They propose an unsupervised keyphrase generation model that combines informativeness and phraseness modules. |
| Outcome: | The proposed model outperforms baseline models and achieves 89% of the performance of a supervised model for top 10 predictions. |
Logic-Regularized Verifier Elicits Reasoning from LLMs (2025.acl-long)
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| Challenge: | Typical verifiers require resource-intensive supervised dataset construction, which is costly and faces limitations in data diversity. |
| Approach: | They propose an unsupervised verifier regularized by logical rules that uses internal activations and logical constraints on multiple reasoning paths. |
| Outcome: | Experiments on 10 datasets show that the proposed verifier outperforms baselines and is comparable to the supervised verifier. |