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Cited by 4

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Conversation Style Transfer using Few-Shot Learning

Roy, Shamik, Shu, Raphael, Pappas, Nikolaos, Mansimov, Elman, Zhang, Yi, Mansour, Saab, Roth, Dan·2023·Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
DOI 10.18653/v1/2023.ijcnlp-main.9
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Figure 1 Transferring the style of an utterance from chatbot to human agent style based on three utterancelevel (single-turn) and conversation-level (multi-turn) examples as input. Additional conversational context helps the style transfer model to yield a more appropriate response as the dialogue contains useful information that can be leveraged during the generation process.
Figure 1 Transferring the style of an utterance from chatbot to human agent style based on three utterancelevel (single-turn) and conversation-level (multi-turn) examples as input. Additional conversational context helps the style transfer model to yield a more appropriate response as the dialogue contains useful information that can be leveraged during the generation process.CC-BY · From the PDF
Figure 2 The proposed two-step in-context learning-based approach for conversation style transfer: (Step 1) The style in the source conversation is reduced and it is converted to a style-free conversation. (Step 2) The style-free conversation is converted to the target style. Both conversion steps are learned in context.
Figure 2 The proposed two-step in-context learning-based approach for conversation style transfer: (Step 1) The style in the source conversation is reduced and it is converted to a style-free conversation. (Step 2) The style-free conversation is converted to the target style. Both conversion steps are learned in context.CC-BY · From the PDF
Image 3
CC-BY · From the PDF
Figure 3 Prompt structure for transferring a source conversation to a style free conversation.
Figure 3 Prompt structure for transferring a source conversation to a style free conversation.CC-BY · From the PDF
Image 5
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Image 6
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Figure 6 Prompt structure for transferring a style free conversation to the target style (in human agent style, H 1 ) using various versions of our model. For simplicity, 3, 2, 1 shots prompts are shown in case of utterance level style transfer, 2-turns conversation level style transfer and 4/5-turns conversation level style transfer, respectively.
Figure 6 Prompt structure for transferring a style free conversation to the target style (in human agent style, H 1 ) using various versions of our model. For simplicity, 3, 2, 1 shots prompts are shown in case of utterance level style transfer, 2-turns conversation level style transfer and 4/5-turns conversation level style transfer, respectively.CC-BY · From the PDF
Image 8
CC-BY · From the PDF
Figure 8 Human evaluation UI for the evaluation of style strength.
Figure 8 Human evaluation UI for the evaluation of style strength.CC-BY · From the PDF
Figure 9 Human evaluation UI for the evaluation of appropriateness.
Figure 9 Human evaluation UI for the evaluation of appropriateness.CC-BY · From the PDF
Figure 10 Human evaluation UI for the evaluation of semantic correctness.
Figure 10 Human evaluation UI for the evaluation of semantic correctness.CC-BY · From the PDF

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Metadata

Authors
Roy, S., Shu, R., Pappas, N., Mansimov, E., Zhang, Y., Mansour, S., & Roth, D.
Venue
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
Publisher
Association for Computational Linguistics
Published
2023
Pages
119-143
DOI
10.18653/v1/2023.ijcnlp-main.9
Cited by
4
References
5

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CoChat. (2026). Citation check: Conversation Style Transfer using Few-Shot Learning. https://app.cochat.ai/papers/10.18653/v1/2023.ijcnlp-main.9
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Roy, S., Shu, R., Pappas, N., Mansimov, E., Zhang, Y., Mansour, S., & Roth, D. (2023). Conversation Style Transfer using Few-Shot Learning. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/2023.ijcnlp-main.9

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