Draft, Command, and Edit: Controllable Text Editing in E-Commerce

by   Kexin Yang, et al.

Product description generation is a challenging and under-explored task. Most such work takes a set of product attributes as inputs then generates a description from scratch in a single pass. However, this widespread paradigm might be limited when facing the dynamic wishes of users on constraining the description, such as deleting or adding the content of a user-specified attribute based on the previous version. To address this challenge, we explore a new draft-command-edit manner in description generation, leading to the proposed new task-controllable text editing in E-commerce. More specifically, we allow systems to receive a command (deleting or adding) from the user and then generate a description by flexibly modifying the content based on the previous version. It is easier and more practical to meet the new needs by modifying previous versions than generating from scratch. Furthermore, we design a data augmentation method to remedy the low resource challenge in this task, which contains a model-based and a rule-based strategy to imitate the edit by humans. To accompany this new task, we present a human-written draft-command-edit dataset called E-cEdits and a new metric "Attribute Edit". Our experimental results show that using the new data augmentation method outperforms baselines to a greater extent in both automatic and human evaluations.


page 1

page 2

page 3

page 4


Edit As You Wish: Video Description Editing with Multi-grained Commands

Automatically narrating a video with natural language can assist people ...

Emotion Selectable End-to-End Text-based Speech Editing

Text-based speech editing allows users to edit speech by intuitively cut...

Text Editing by Command

A prevailing paradigm in neural text generation is one-shot generation, ...

I2Edit: Towards Multi-turn Interactive Image Editing via Dialogue

Although there have been considerable research efforts on controllable f...

Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation

Unsupervised text attribute transfer automatically transforms a text to ...

A Retrieve-and-Edit Framework for Predicting Structured Outputs

For the task of generating complex outputs such as source code, editing ...

Automatic Generation of Chinese Short Product Titles for Mobile Display

This paper studies the problem of automatically extracting a short title...

Please sign up or login with your details

Forgot password? Click here to reset