| Challenge: | Existing open domain response generation models are limited to paired data, but are less explored in real-world applications. |
| Approach: | They propose to train a neural response generation model with unpaired data and paired data as prior. |
| Outcome: | The proposed model outperforms state-of-the-art models in both automatic and human evaluation when only a few pairs are available. |
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| Challenge: | Encoder-decoder models are uninterpretable and difficult to control in terms of content. |
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| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
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| Challenge: | Existing methods to generate valid and fluent questions from text are limited and insufficient for training. |
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A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation (2021.emnlp-main)
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| Challenge: | Existing methods for generating open-domain dialogue systems underutilize training data. |
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| Challenge: | a low-resource natural language generation task requires a large number of examples to generate outputs and outputs. |
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Fine-grained Contrastive Learning for Definition Generation (2022.aacl-main)
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| Challenge: | Pre-trained language models have been widely used in open-domain dialogue generation. |
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A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (2021.naacl-main)
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| Challenge: | a growing body of work is focused on improving performance in low-resource settings . a goal of this study is to explain how these methods differ in their requirements . |
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