Papers with unconditional model
DiffuDetox: A Mixed Diffusion Model for Text Detoxification (2023.findings-acl)
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Griffin Floto, Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Zhenwei Tang, Ali Pesaranghader, Manasa Bharadwaj, Scott Sanner
| Challenge: | Existing text generation models that reduce toxicity of toxic text are inadequate for text detoxification tasks. |
| Approach: | They propose a conditional and unconditional diffusion model for text detoxification . conditional model takes toxic text as condition and reduces its toxicity . experimental results show the model achieves human-level fluency . |
| Outcome: | The proposed model reduces toxic text and produces diverse sentences . it can be used to train other models and ensure fluency . |
Learning to Discover, Ground and Use Words with Segmental Neural Language Models (P19-1)
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| Challenge: | Existing models of word learning do not account for the long-range dependencies manifest in language and that are easily captured by recurrent neural networks. |
| Approach: | They propose a segmental neural language model that unifies word discovery, learning how words fit together to form sentences, and by conditioning the model on visual context, how words’ meanings ground in representations of nonlinguistic modalities. |
| Outcome: | The proposed model learns predictive distributions better than character LSTM models, discovers words competitively with nonparametric Bayesian word segmentation models, and improves on both. |