# Controllable and Diverse Text Generation in E-commerce arXiv: https://arxiv.org/abs/2102.11497 ยท HTML: https://mlsanity.com/paper/2102.11497/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 44 references that are in the map. - First posted on arXiv 23 Feb 2021. ## Builds on (most cited first, 25 of 25) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014) - [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015) - [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017) - [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017) - [Texygen: A Benchmarking Platform for Text Generation Models](https://mlsanity.com/paper/1802.01886/) (2018) - [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016) - [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017) - [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://mlsanity.com/paper/1703.10960/) (2017) - [Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing](https://mlsanity.com/paper/1903.10145/) (2019) - [Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization](https://mlsanity.com/paper/1809.05972/) (2018) - [Adversarial Learning for Neural Dialogue Generation](https://mlsanity.com/paper/1701.06547/) (2017) - [Long Text Generation via Adversarial Training with Leaked Information](https://mlsanity.com/paper/1709.08624/) (2017) - [Variational Autoencoder with Arbitrary Conditioning](https://mlsanity.com/paper/1806.02382/) (2018) - [ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis](https://mlsanity.com/paper/2010.06119/) (2020) - [Adversarial Ranking for Language Generation](https://mlsanity.com/paper/1705.11001/) (2017) - [Topic-Guided Variational Autoencoders for Text Generation](https://mlsanity.com/paper/1903.07137/) (2019) - [DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text](https://mlsanity.com/paper/1802.01345/) (2018) - [A Hybrid Convolutional Variational Autoencoder for Text Generation](https://mlsanity.com/paper/1702.02390/) (2017) - [Towards Knowledge-Based Personalized Product Description Generation in E-commerce](https://mlsanity.com/paper/1903.12457/) (2019) - [Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings](https://mlsanity.com/paper/1606.05491/) (2016) - [Towards Controllable and Personalized Review Generation](https://mlsanity.com/paper/1910.03506/) (2019) - [Improving Variational Encoder-Decoders in Dialogue Generation](https://mlsanity.com/paper/1802.02032/) (2018) - [Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation](https://mlsanity.com/paper/1809.00794/) (2018) ## Built on by (most cited first, 13 of 13) - [Controllable Data Generation by Deep Learning: A Review](https://mlsanity.com/paper/2207.09542/) (2022) - [Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation](https://mlsanity.com/paper/2401.10186/) (2024) - [DeepGen: Diverse Search Ad Generation and Real-Time Customization](https://mlsanity.com/paper/2208.03438/) (2022) - [Natural Language Generation for Advertising: A Survey](https://mlsanity.com/paper/2306.12719/) (2023) - [What Makes Data-to-Text Generation Hard for Pretrained Language Models?](https://mlsanity.com/paper/2205.11505/) (2022) - [Diversifying Question Generation over Knowledge Base via External Natural Questions](https://mlsanity.com/paper/2309.14362/) (2023) - [Controlling keywords and their positions in text generation](https://mlsanity.com/paper/2304.09516/) (2023) - [Efficient Language Modeling for Low-Resource Settings with Hybrid RNN-Transformer Architectures](https://mlsanity.com/paper/2502.00617/) (2025) - [Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models](https://mlsanity.com/paper/2605.12138/) (2026) - [Learning to Diversify for Product Question Generation](https://mlsanity.com/paper/2207.02534/) (2022) - [Draft, Command, and Edit: Controllable Text Editing in E-Commerce](https://mlsanity.com/paper/2208.05623/) (2022) - [TegFormer: Topic-to-Essay Generation with Good Topic Coverage and High Text Coherence](https://mlsanity.com/paper/2212.13456/) (2022) - [Safety as a Constraint: Fine-Tuning a LLM Recommender to Explain Itself](https://mlsanity.com/paper/2609.13657/) (2026) ## Abstract (arXiv) In E-commerce, a key challenge in text generation is to find a good trade-off between word diversity and accuracy (relevance) in order to make generated text appear more natural and human-like. In order to improve the relevance of generated results, conditional text generators were developed that use input keywords or attributes to produce the corresponding text. Prior work, however, do not finely control the diversity of automatically generated sentences. For example, it does not control the order of keywords to put more relevant ones first. Moreover, it does not explicitly control the balance between diversity and accuracy. To remedy these problems, we propose a fine-grained controllable generative model, called~textitApex, that uses an algorithm borrowed from automatic control (namely, a variant of the textitproportional, integral, and derivative (PID) controller) to precisely manipulate the diversity/accuracy trade-off of generated text. The algorithm is injected into a Conditional Variational Autoencoder (CVAE), allowing textitApex to control both (i) the order of keywords in the generated sentences (conditioned on the input keywords and their order), and (ii) the trade-off between diversity and accuracy. Evaluation results on real-world datasets show that the proposed method outperforms existing generative models in terms of diversity and relevance. Apex is currently deployed to generate production descriptions and item recommendation reasons in Taobao owned by Alibaba, the largest E-commerce platform in China. The A/B production test results show that our method improves click-through rate (CTR) by 13.17% compared to the existing method for production descriptions. For item recommendation reason, it is able to increase CTR by 6.89% and 1.42% compared to user reviews and top-K item recommendation without reviews, respectively.