Large Scale Author Obfuscation Using Siamese Variational Auto-Encoder: The SiamAO System (2020.starsem-1)
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| Challenge: | Existing approaches to author obfuscation are largely heuristic, but they can be used to attack author identification. |
| Approach: | They propose a deep learning architecture for constructing adversarial examples against similarity-based learners and explore its application to author obfuscation. |
| Outcome: | The proposed architectures show that they can be used to attack author obfuscation . the proposed architecture shows that it can be applied to obliquacy of text . |
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| Challenge: | Neural networks are vulnerable to adversarial examples that have been mixed with certain perturbations. |
| Approach: | They propose a novel adversarial training method that perturbs the embedding matrix instead of word vectors to differentiate the roles of passages and questions. |
| Outcome: | The proposed method is effective universally and further improves the performance of MRC tasks. |
Leveraging Three Types of Embeddings from Masked Language Models in Idiom Token Classification (2022.starsem-1)
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| Challenge: | Recent research shows that contextualized word embeddings can give promising results for idiom token classification. |
| Approach: | They propose to leverage contextualized word embeddings from masked language models to improve idiom token classification. |
| Outcome: | The proposed method improves idiom token classification for English and Japanese datasets. |
Improving Word Sense Induction through Adversarial Forgetting of Morphosyntactic Information (2024.starsem-1)
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| Challenge: | Contextualized word representations from pre-trained language models encode more information than is necessary for the identification of word senses and some of this information affect performance negatively in unsupervised settings. |
| Approach: | They propose to use a framework to erase specific information from pre-trained word models and create feature-invariant representations that are invariant to these ‘nuisance features’. |
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Testing Paraphrase Models on Recognising Sentence Pairs at Different Degrees of Semantic Overlap (2023.starsem-1)
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| Challenge: | Existing models for paraphrase detection are not suitable for many applications . existing datasets ignore and fail to test models in this setup . |
| Approach: | They propose to use adversarial paradigms to test paraphrase detection models . they propose to examine the sensitivity to different degrees of semantic overlap . |
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A Trip Towards Fairness: Bias and De-Biasing in Large Language Models (2024.starsem-1)
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| Challenge: | a little or a large bias in CtB-LLMs may cause huge harm . LLaMA and OPT families have an important bias in gender, race, religion, and profession. |
| Approach: | They propose to debiase three families of Very Large-Language Models with LORA to reduce bias by 4.12 points in the normalized stereotype score. |
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A Generative Approach for Mitigating Structural Biases in Natural Language Inference (2022.starsem-1)
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| Challenge: | Natural language inference datasets contain artifacts and biases that allow models to perform poorly by using a biased subset of the input without considering the remainder features. |
| Approach: | They reformulate a natural language inference task as a generative task . they find that this approach is highly robust to large amounts of bias . |
| Outcome: | The proposed model is highly robust to large amounts of bias. |
DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models (2022.starsem-1)
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| Challenge: | ArgumentAnalyst is a multi-dimensional, modular framework for performing deep argument analysis using existing pre-trained language models (PTLMs). |
| Approach: | They propose a multi-dimensional, modular framework for performing deep argument analysis using current pre-trained language models (PTLMs) ArgumentAnalyst reconstructs argumentative texts as valid arguments by inserting missing premises and conclusions, formalizing inferences, and coherently linking the reconstruction to the source text. |
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Inducing Language-Agnostic Multilingual Representations (2021.starsem-1)
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| Challenge: | Cross-lingual representations have the potential to make NLP techniques available to the vast majority of languages in the world, but they currently require large pretraining corpora or access to typologically similar languages. |
| Approach: | They propose to remove language identity signals from multilingual embeddings by re-aligning vector spaces of target languages to a pivot source language and removing language-specific means and variances. |
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Recovering Lexically and Semantically Reused Texts (2021.starsem-1)
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| Challenge: | Writers often repurpose material from existing texts when composing new documents. |
| Approach: | They propose to use local text reuse detection to detect localized regions of lexically or semantically similar text embedded in otherwise unrelated material. |
| Outcome: | The proposed methods perform better on three LTRD tasks, detecting plagiarism, modeling journalists’ use of press releases, and identifying scientists’ citation of earlier papers. |
Length-Aware Multi-Kernel Transformer for Long Document Classification (2024.starsem-1)
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| Challenge: | Existing SOTA models segment long texts into equal-length snippets, but they have new challenges of context fragmentation and generalizability due to sentence boundaries and varying text lengths. |
| Approach: | They propose a Length-Aware Multi-Kernel Transformer to encode long documents by transformers and vectorize text length by the kernels to promote model robustness over varying document lengths. |
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