Minlie Huang, Fei Huang, Chen Wu, Zikai Chen, Qihan Guo, Xiaoyan Zhu
We lifted 2 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| fastnlp/style-transformer | — | 0 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| EmbeddingLayer | Not yet run | fastnlp/style-transformer/models/transformer.py pointer only (licence: NONE) · get_code("d8ee2a61c05425ae") |
| StyleTransformer | Not yet run | fastnlp/style-transformer/models/transformer.py pointer only (licence: NONE) · get_code("c8fa627713b0b9da") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Autoregressive models have been widely used in unsupervised text style transfer. Despite their success, these models still suffer from the content preservation problem that they usually ignore part of the source sentence and generate some irrelevant words with strong styles. In this paper, we propose a Non-Autoregressive generator for unsupervised text Style Transfer (NAST), which alleviates the problem from two aspects. First, we observe that most words in the transferred sentence can be aligned with related words in the source sentence, so we explicitly model word alignments to suppress irrelevant words. Second, existing models trained with the cycle loss align sentences in two stylistic text spaces, which lacks fine-grained control at the word level. The proposed non-autoregressive generator focuses on the connections between aligned words, which learns the word-level transfer between styles. For experiments, we integrate the proposed generator into two base models and evaluate them on two style transfer tasks. The results show that NAST can significantly improve the overall performance and provide explainable word alignments. Moreover, the nonautoregressive generator achieves over 10x speedups at inference. Our codes are available at https://github.com/thu-coai/NAST.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2106.02210")
get_code_for_paper("2106.02210")
have("2106.02210")
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