Papers with semantic control
Improving Semantic Control in Discrete Latent Spaces with Transformer Quantized Variational Autoencoders (2024.findings-eacl)
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| Challenge: | Recent work has struggled to achieve consistent results due to the inevitable loss of semantic information in the variational bottleneck and limited control over the decoding mechanism. |
| Approach: | They propose a model that leverages the controllability of VQVAE to guide the self-attention mechanism in Transformer-based VAEs to improve semantic control and generation. |
| Outcome: | The proposed model outperforms existing state-of-the-art VAE models in terms of control and preservation of semantic information across different tasks. |
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. |
Consistent Autoformalization for Constructing Mathematical Libraries (2024.emnlp-main)
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| Challenge: | Autoformalization is the task of automatically translating mathematical content written in natural language to a formal language expression. |
| Approach: | They propose to use three mechanisms to improve autoformalization quality . they propose to combine most-similar retrieval augmented generation, denoising steps and auto-correction with syntax error feedback to improve syntactic, terminological and semantic control. |
| Outcome: | The proposed mechanisms can deliver syntactically, terminologically and semantically more consistent results across different models. |
VIMI: Grounding Video Generation through Multi-modal Instruction (2024.emnlp-main)
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Yuwei Fang, Willi Menapace, Aliaksandr Siarohin, Tsai-Shien Chen, Kuan-Chieh Wang, Ivan Skorokhodov, Graham Neubig, Sergey Tulyakov
| Challenge: | Existing text-to-video diffusion models rely on text-only encoders for their pretraining, restricting their versatility and application in multimodal integration. |
| Approach: | They propose a multimodal conditional video generation framework for pretraining on augmented text prompts and then utilize a two-stage training strategy to enable diverse video generation tasks within a model. |
| Outcome: | The proposed model can synthesize consistent and temporally coherent videos with large motion while retaining the semantic control. |