Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation

2019-11-01

Conference on Empirical Methods in Natural Language Processing

AI Summary

To address factual inconsistency and attribute deviation in product description generation for e-commerce, this paper proposes an attribute-faithful controllable generation method. The core innovation lies in an entity-label-guided ELSTM decoder unit integrated with a keyword memory mechanism: fine-grained word-level entity labels serve as explicit control signals, jointly leveraged via label-aware attention and a dynamic keyword memory bank to enable attribute-aware generation. The method synergistically combines entity label embeddings, RNN-based architecture, and structured memory to significantly enhance the fidelity of generated text with respect to product attributes. Evaluated on a large-scale real-world e-commerce dataset, the approach achieves a 25% improvement in attribute fidelity over prior methods, while also attaining state-of-the-art performance on standard automatic metrics (BLEU, ROUGE) and human evaluations.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Feature Construction/Reformulation
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information. However, little attention has been paid to this problem. To bridge this gap we propose a model named Fidelity-oriented Product Description Generator (FPDG). FPDG takes the entity label of each word into account, since the product attribute information is always conveyed by entity words. Specifically, we first propose a Recurrent Neural Network (RNN) decoder based on the Entity-label-guided Long Short-Term Memory (ELSTM) cell, taking both the embedding and the entity label of each word as input. Second, we establish a keyword memory that stores the entity labels as keys and keywords as values, and FPDG will attend to keywords through attending to their entity labels. Experiments conducted a large-scale real-world product description dataset show that our model achieves the state-of-the-art performance in terms of both traditional generation metrics as well as human evaluations. Specifically, FPDG increases the fidelity of the generated descriptions by 25%.

Problem

• Generating faithful product descriptions using entity labels.
• Improving fidelity in product description generation tasks.
• Developing a model to enhance description accuracy and relevance.

Innovation

• Entity-label-guided LSTM for word processing
• Keyword memory for entity label-keyword mapping
• 25% fidelity increase in product descriptions