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            <namePart type="given">Donia</namePart>
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    <abstract>Dialogue systems using deep learning have achieved generation of fluent response sentences to user utterances. Nevertheless, they tend to produce responses that are not diverse and which are less context-dependent. To address these shortcomings, we propose a new loss function, an Inverse N-gram loss (INF), which incorporates contextual fluency and diversity at the same time by a simple formula. Our INF loss can adjust its loss dynamically by a weight using the inverse frequency of the tokens’ n-gram applied to Softmax Cross-Entropy loss, so that rare tokens appear more likely while retaining the fluency of the generated sentences. We trained Transformer using English and Japanese Twitter replies as single-turn dialogues using different loss functions. Our INF loss model outperformed the baselines of SCE loss and ITF loss models in automatic evaluations such as DIST-N and ROUGE, and also achieved higher scores on our human evaluations of coherence and richness.</abstract>
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%0 Conference Proceedings
%T Diverse dialogue generation with context dependent dynamic loss function
%A Ueyama, Ayaka
%A Kano, Yoshinobu
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F ueyama-kano-2020-diverse
%X Dialogue systems using deep learning have achieved generation of fluent response sentences to user utterances. Nevertheless, they tend to produce responses that are not diverse and which are less context-dependent. To address these shortcomings, we propose a new loss function, an Inverse N-gram loss (INF), which incorporates contextual fluency and diversity at the same time by a simple formula. Our INF loss can adjust its loss dynamically by a weight using the inverse frequency of the tokens’ n-gram applied to Softmax Cross-Entropy loss, so that rare tokens appear more likely while retaining the fluency of the generated sentences. We trained Transformer using English and Japanese Twitter replies as single-turn dialogues using different loss functions. Our INF loss model outperformed the baselines of SCE loss and ITF loss models in automatic evaluations such as DIST-N and ROUGE, and also achieved higher scores on our human evaluations of coherence and richness.
%R 10.18653/v1/2020.coling-main.364
%U https://aclanthology.org/2020.coling-main.364/
%U https://doi.org/10.18653/v1/2020.coling-main.364
%P 4123-4127

Markdown (Informal)

[Diverse dialogue generation with context dependent dynamic loss function](https://aclanthology.org/2020.coling-main.364/) (Ueyama & Kano, COLING 2020)

• Diverse dialogue generation with context dependent dynamic loss function (Ueyama & Kano, COLING 2020)

ACL

• Ayaka Ueyama and Yoshinobu Kano. 2020. Diverse dialogue generation with context dependent dynamic loss function. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4123–4127, Barcelona, Spain (Online). International Committee on Computational Linguistics.