UoB at SemEval-2021 Task 5: Extending Pre-Trained Language Models to Include Task and Domain-Specific Information for Toxic Span Prediction

• URL: http://arxiv.org/abs/2110.03730v1
• Date: Thu, 7 Oct 2021 18:29:06 GMT
• Title: UoB at SemEval-2021 Task 5: Extending Pre-Trained Language Models to Include Task and Domain-Specific Information for Toxic Span Prediction
• Authors: Erik Yan and Harish Tayyar Madabushi
• Abstract summary: Toxicity is pervasive in social media and poses a major threat to the health of online communities. Recent introduction of pre-trained language models, which have achieved state-of-the-art results in many NLP tasks, has transformed the way in which we approach natural language processing.
• Score: 0.8376091455761259
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Toxicity is pervasive in social media and poses a major threat to the health of online communities. The recent introduction of pre-trained language models, which have achieved state-of-the-art results in many NLP tasks, has transformed the way in which we approach natural language processing. However, the inherent nature of pre-training means that they are unlikely to capture task-specific statistical information or learn domain-specific knowledge. Additionally, most implementations of these models typically do not employ conditional random fields, a method for simultaneous token classification. We show that these modifications can improve model performance on the Toxic Spans Detection task at SemEval-2021 to achieve a score within 4 percentage points of the top performing team.

Related papers

• Ensembling Finetuned Language Models for Text Classification [55.15643209328513]
  Finetuning is a common practice across different communities to adapt pretrained models to particular tasks. ensembles of neural networks are typically used to boost performance and provide reliable uncertainty estimates. We present a metadataset with predictions from five large finetuned models on six datasets and report results of different ensembling strategies.
  arXiv  Detail & Related papers  (2024-10-25T09:15:54Z)
• Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identification [19.893213508284813]
  Self-supervised adaptive pre-training is proposed to adapt the pre-trained model to the target domain and languages of the downstream task. We show that SAPT improves XLSR performance on the FLEURS benchmark with substantial gains up to 40.1% for under-represented languages.
  arXiv  Detail & Related papers  (2023-12-12T14:58:08Z)
• Adding Instructions during Pretraining: Effective Way of Controlling Toxicity in Language Models [29.505176809305095]
  We propose two novel pretraining data augmentation strategies that significantly reduce model toxicity without compromising its utility. Our two strategies are: (1) MEDA: adds raw toxicity score as meta-data to the pretraining samples, and (2) INST: adds instructions to those samples indicating their toxicity. Our results indicate that our best performing strategy (INST) substantially reduces the toxicity probability up to 61% while preserving the accuracy on five benchmark NLP tasks.
  arXiv  Detail & Related papers  (2023-02-14T23:00:42Z)
• Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization [94.4409074435894]
  We propose a novel and effective fine-tuning framework, named Layerwise Noise Stability Regularization (LNSR) Specifically, we propose to inject the standard Gaussian noise and regularize hidden representations of the fine-tuned model. We demonstrate the advantages of the proposed method over other state-of-the-art algorithms including L2-SP, Mixout and SMART.
  arXiv  Detail & Related papers  (2022-06-12T04:42:49Z)
• A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis [90.24921443175514]
  We focus on aspect-based sentiment analysis, which involves extracting aspect term, category, and predicting their corresponding polarities. We propose to reformulate the extraction and prediction tasks into the sequence generation task, using a generative language model with unidirectional attention. Our approach outperforms the previous state-of-the-art (based on BERT) on average performance by a large margins in few-shot and full-shot settings.
  arXiv  Detail & Related papers  (2022-04-11T18:31:53Z)
• UPB at SemEval-2021 Task 5: Virtual Adversarial Training for Toxic Spans Detection [0.7197592390105455]
  Semeval-2021, Task 5 - Toxic Spans Detection is based on a novel annotation of a subset of the Jigsaw Unintended Bias dataset. For this task, participants had to automatically detect character spans in short comments that render the message as toxic. Our model considers applying Virtual Adversarial Training in a semi-supervised setting during the fine-tuning process of several Transformer-based models.
  arXiv  Detail & Related papers  (2021-04-17T19:42:12Z)
• Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning [74.25168207651376]
  Fine-tuning pre-trained language models to downstream cross-lingual tasks has shown promising results. We leverage continual learning to preserve the cross-lingual ability of the pre-trained model when we fine-tune it to downstream tasks. Our methods achieve better performance than other fine-tuning baselines on the zero-shot cross-lingual part-of-speech tagging and named entity recognition tasks.
  arXiv  Detail & Related papers  (2020-04-29T14:07:18Z)
• Don't Stop Pretraining: Adapt Language Models to Domains and Tasks [81.99843216550306]
  We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks. A second phase of pretraining in-domain (domain-adaptive pretraining) leads to performance gains. Adapting to the task's unlabeled data (task-adaptive pretraining) improves performance even after domain-adaptive pretraining.
  arXiv  Detail & Related papers  (2020-04-23T04:21:19Z)
• Pre-training Text Representations as Meta Learning [113.3361289756749]
  We introduce a learning algorithm which directly optimize model's ability to learn text representations for effective learning of downstream tasks. We show that there is an intrinsic connection between multi-task pre-training and model-agnostic meta-learning with a sequence of meta-train steps.
  arXiv  Detail & Related papers  (2020-04-12T09:05:47Z)
• CALM: Continuous Adaptive Learning for Language Modeling [18.72860206714457]
  Training large language representation models has become a standard in the natural language processing community. We demonstrate that in practice these pre-trained models present performance deterioration in the form of catastrophic forgetting. We propose CALM, Continuous Adaptive Learning for Language Modeling: techniques to render models which retain knowledge across multiple domains.
  arXiv  Detail & Related papers  (2020-04-08T03:51:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.