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        <title>Composing Ci with Reinforced Non-autoregressive Text Generation</title>
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            <namePart type="given">Yoav</namePart>
            <namePart type="family">Goldberg</namePart>
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        <name type="personal">
            <namePart type="given">Zornitsa</namePart>
            <namePart type="family">Kozareva</namePart>
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    <abstract>Composing Ci (also widely known as Song Ci), a special type of classical Chinese poetry, requires to follow particular format once their tune patterns are given. To automatically generate a well-formed Ci, text generation systems should strictly take into account pre-defined rigid formats (e.g., length and rhyme). Yet, most existing approaches regard Ci generation as a conventional sequence-to-sequence task and use autoregressive models, while it is challenging for such models to properly handle the constraints (according to tune patterns) of Ci during the generation process. Moreover, consider that with the format prepared, Ci generation can be operated by an efficient synchronous process, where autoregressive models are limited in doing so since they follow the character-by-character generation protocol. Therefore, in this paper, we propose to compose Ci through a non-autoregressive approach, which not only ensure that the generation process accommodates tune patterns by controlling the rhythm and essential meaning of each sentence, but also allow the model to perform synchronous generation. In addition, we further improve our approach by applying reinforcement learning to the generation process with the rigid constraints of Ci as well as the diversity in content serving as rewards, so as to further maintain the format and content requirement. Experiments on a collected Ci dataset confirm that our proposed approach outperforms strong baselines and previous studies in terms of both automatic evaluation metrics and human judgements.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-main.486</identifier>
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%0 Conference Proceedings
%T Composing Ci with Reinforced Non-autoregressive Text Generation
%A Song, Yan
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F song-2022-composing
%X Composing Ci (also widely known as Song Ci), a special type of classical Chinese poetry, requires to follow particular format once their tune patterns are given. To automatically generate a well-formed Ci, text generation systems should strictly take into account pre-defined rigid formats (e.g., length and rhyme). Yet, most existing approaches regard Ci generation as a conventional sequence-to-sequence task and use autoregressive models, while it is challenging for such models to properly handle the constraints (according to tune patterns) of Ci during the generation process. Moreover, consider that with the format prepared, Ci generation can be operated by an efficient synchronous process, where autoregressive models are limited in doing so since they follow the character-by-character generation protocol. Therefore, in this paper, we propose to compose Ci through a non-autoregressive approach, which not only ensure that the generation process accommodates tune patterns by controlling the rhythm and essential meaning of each sentence, but also allow the model to perform synchronous generation. In addition, we further improve our approach by applying reinforcement learning to the generation process with the rigid constraints of Ci as well as the diversity in content serving as rewards, so as to further maintain the format and content requirement. Experiments on a collected Ci dataset confirm that our proposed approach outperforms strong baselines and previous studies in terms of both automatic evaluation metrics and human judgements.
%R 10.18653/v1/2022.emnlp-main.486
%U https://aclanthology.org/2022.emnlp-main.486
%U https://doi.org/10.18653/v1/2022.emnlp-main.486
%P 7219-7229

Markdown (Informal)

[Composing Ci with Reinforced Non-autoregressive Text Generation](https://aclanthology.org/2022.emnlp-main.486) (Song, EMNLP 2022)

• Composing Ci with Reinforced Non-autoregressive Text Generation (Song, EMNLP 2022)

ACL

• Yan Song. 2022. Composing Ci with Reinforced Non-autoregressive Text Generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 7219–7229, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.