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    <abstract>Recent language modeling performance has been greatly improved by the use of external memory. This memory encodes the context so that similar contexts can be recalled during decoding. This similarity depends on how the model learns to encode context, which can be altered to include other attributes, such as style. We construct and evaluate an architecture for this purpose, using corpora annotated for politeness, formality, and toxicity. Through extensive experiments and human evaluation we demonstrate the potential of our method to generate text while controlling style. We find that style-specific datastores improve generation performance, though results vary greatly across styles, and the effect of pretraining data and specific styles should be explored in future work.</abstract>
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%0 Conference Proceedings
%T Nearest Neighbor Language Models for Stylistic Controllable Generation
%A Trotta, Severino
%A Flek, Lucie
%A Welch, Charles
%Y Bosselut, Antoine
%Y Chandu, Khyathi
%Y Dhole, Kaustubh
%Y Gangal, Varun
%Y Gehrmann, Sebastian
%Y Jernite, Yacine
%Y Novikova, Jekaterina
%Y Perez-Beltrachini, Laura
%S Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F trotta-etal-2022-nearest
%X Recent language modeling performance has been greatly improved by the use of external memory. This memory encodes the context so that similar contexts can be recalled during decoding. This similarity depends on how the model learns to encode context, which can be altered to include other attributes, such as style. We construct and evaluate an architecture for this purpose, using corpora annotated for politeness, formality, and toxicity. Through extensive experiments and human evaluation we demonstrate the potential of our method to generate text while controlling style. We find that style-specific datastores improve generation performance, though results vary greatly across styles, and the effect of pretraining data and specific styles should be explored in future work.
%R 10.18653/v1/2022.gem-1.25
%U https://aclanthology.org/2022.gem-1.25
%U https://doi.org/10.18653/v1/2022.gem-1.25
%P 295-305

Markdown (Informal)

[Nearest Neighbor Language Models for Stylistic Controllable Generation](https://aclanthology.org/2022.gem-1.25) (Trotta et al., GEM 2022)

• Nearest Neighbor Language Models for Stylistic Controllable Generation (Trotta et al., GEM 2022)

ACL

• Severino Trotta, Lucie Flek, and Charles Welch. 2022. Nearest Neighbor Language Models for Stylistic Controllable Generation. In Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), pages 295–305, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.