Learning to Select Context in a Hierarchical and Global Perspective for Open-domain Dialogue Generation

Shen, Lei; Zhan, Haolan; Shen, Xipeng; Feng, Yang

doi:10.48550/arxiv.2102.09282

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Personalized content generation has attracted research interest in various domains, e.g., the automatic generation of marketing messages (Roy et al 2015;Chen et al 2020), persuasive message (Ding and Pan 2016), poetry generation (Shen, Guo, and Chen 2020), argument generation (Carenini and Moore 2006) and dialogue generation (Shen and Feng 2020;Feng et al 2020a;Shen, Feng, and Zhan 2019;Shen et al 2021). With the support of user preferences, the effectiveness has increases.…”

Section: Personalized Content Generationmentioning

confidence: 99%

Probing Product Description Generation via Posterior Distillation

Zhan

Zhang

Chen

et al. 2021

AAAI

Self Cite

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In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks. High-quality customer reviews can be considered as an ideal source to mine user-cared aspects. However, in reality, a large number of new products (known as long-tailed commodities) cannot gather sufficient amount of customer reviews, which brings a big challenge in the product description generation task. Existing works tend to generate the product description solely based on item information, i.e., product attributes or title words, which leads to tedious contents and cannot attract customers effectively. To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews. Specifically, we first extend the self-attentive Transformer encoder to encode product titles and attributes. Then, we apply an adaptive posterior distillation module to utilize useful review information, which integrates user-cared aspects to the generation process. Finally, we apply a Transformer-based decoding phase with copy mechanism to automatically generate the product description. Besides, we also collect a large-scare Chinese product description dataset to support our work and further research in this field. Experimental results show that our model is superior to traditional generative models in both automatic indicators and human evaluation.

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Section: Personalized Content Generationmentioning

confidence: 99%

Probing Product Description Generation via Posterior Distillation

Zhan

Zhang

Chen

et al. 2021

AAAI

Self Cite

View full text Add to dashboard Cite

show abstract

“…Personalized content generation has attracted research interest in various domains, e.g., E-commerce (Zhao, Chen, and Yin 2019;Chen, Zhao, and Yin 2019), the automatic generation of marketing messages (Roy et al 2015;Chen et al 2020), persuasive message (Ding and Pan 2016;Zhang et al 2018), poetry generation (Shen, Guo, and Chen 2020), argument generation (Carenini and Moore 2006) and dialogue generation (Shen and Feng 2020;Feng et al 2020a;Shen, Feng, and Zhan 2019;Shen et al 2021;Cai et al 2020;Liu et al 2020). With the support of user preferences, the effectiveness has increases.…”

Section: Personalized Content Generationmentioning

confidence: 99%

Probing Product Description Generation via Posterior Distillation

Zhan¹,

Zhang²,

Chen³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks. High-quality customer reviews can be considered as an ideal source to mine user-cared aspects. However, in reality, a large number of new products (known as long-tailed commodities) cannot gather sufficient amount of customer reviews, which brings a big challenge in the product description generation task. Existing works tend to generate the product description solely based on item information, i.e., product attributes or title words, which leads to tedious contents and cannot attract customers effectively. To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews. Specifically, we first extend the selfattentive Transformer encoder to encode product titles and attributes. Then, we apply an adaptive posterior distillation module to utilize useful review information, which integrates user-cared aspects to the generation process. Finally, we apply a Transformer-based decoding phase with copy mechanism to automatically generate the product description. Besides, we also collect a large-scare Chinese product description dataset to support our work and further research in this field. Experimental results show that our model is superior to traditional generative models in both automatic indicators and human evaluation.

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GTM: A Generative Triple-Wise Model for Conversational Question Generation

Shen

Meng

Zhang

et al. 2021

Preprint

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Generating some appealing questions in opendomain conversations is an effective way to improve human-machine interactions and lead the topic to a broader or deeper direction. To avoid dull or deviated questions, some researchers tried to utilize answer, the "future" information, to guide question generation. However, they separate a post-questionanswer (PQA) triple into two parts: postquestion (PQ) and question-answer (QA) pairs, which may hurt the overall coherence. Besides, the QA relationship is modeled as a one-to-one mapping that is not reasonable in open-domain conversations. To tackle these problems, we propose a generative triple-wise model with hierarchical variations for open-domain conversational question generation (CQG). Latent variables in three hierarchies are used to represent the shared background of a triple and one-to-many semantic mappings in both PQ and QA pairs. Experimental results on a largescale CQG dataset show that our method significantly improves the quality of questions in terms of fluency, coherence and diversity over competitive baselines.

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Learning to Select Context in a Hierarchical and Global Perspective for Open-domain Dialogue Generation

Cited by 3 publications

References 18 publications

Probing Product Description Generation via Posterior Distillation

Probing Product Description Generation via Posterior Distillation

Probing Product Description Generation via Posterior Distillation

GTM: A Generative Triple-Wise Model for Conversational Question Generation

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