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Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a top-down tree-structured decoder instead of a chain-structured decoder | X2Tree dialogue generation · Tencent Weibo (300 sampled posts) | Level-3 acceptable responses (%): 54.38 -> 65.53 | Helped |
| Use a top-down tree-structured decoder instead of a chain-structured decoder | X2Tree dialogue generation · Tencent Weibo (300 sampled posts) | Level-2 irrelevant responses (%): 44.98 -> 34.02 | Helped |
| Use a top-down tree-structured decoder instead of a chain-structured decoder | X2Tree dialogue generation · Tencent Weibo (300 sampled posts) | Level-1 ungrammatical responses (%): 0.64 -> 0.44 | Helped |
| Use a top-down tree-structured decoder instead of a chain-structured decoder | X2Tree dialogue generation · Tencent Weibo (300 sampled posts) | BLEU-4: 13.89 -> 15.87 | Helped |
Closest papers by meaning
- Unsupervised Learning of Discourse Structures using a Tree Autoencoder (AAAI 2021 · similarity 0.88)
- Tree-structured decoding with doubly-recurrent neural networks (ICLR 2017 · similarity 0.87)
- Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models (AAAI 2016 · similarity 0.87)
- A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues (AAAI 2017 · similarity 0.86)
- Tree-Structured Attention with Hierarchical Accumulation (ICLR 2020 · similarity 0.86)
- From Retrieval to Generation: A Simple and Unified Generative Model for End-to-End Task-Oriented Dialogue (AAAI 2024 · similarity 0.84)
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