# AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience

arXiv: https://arxiv.org/abs/1801.05032 · HTML: https://mlsanity.com/paper/1801.05032/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2018.

## Built on by (most cited first, 26 of 26)

- [ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs](https://mlsanity.com/paper/2110.13715/) (2021)
- [Structural Pre-training for Dialogue Comprehension](https://mlsanity.com/paper/2105.10956/) (2021)
- [Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots](https://mlsanity.com/paper/2103.09534/) (2021)
- [Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehension](https://mlsanity.com/paper/2109.04066/) (2021)
- [Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph](https://mlsanity.com/paper/2209.15214/) (2022)
- [Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot](https://mlsanity.com/paper/2108.07935/) (2021)
- [Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge](https://mlsanity.com/paper/2102.05474/) (2021)
- [Product Question Answering in E-Commerce: A Survey](https://mlsanity.com/paper/2302.08092/) (2023)
- [xPQA: Cross-Lingual Product Question Answering across 12 Languages](https://mlsanity.com/paper/2305.09249/) (2023)
- [Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language Conversations](https://mlsanity.com/paper/2307.16013/) (2023)
- [A Survey of Ontology Expansion for Conversational Understanding](https://mlsanity.com/paper/2410.15019/) (2024)
- [Conversational Query Rewriting with Self-supervised Learning](https://mlsanity.com/paper/2102.04708/) (2021)
- [Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning](https://mlsanity.com/paper/2110.00159/) (2021)
- [K-AID: Enhancing Pre-trained Language Models with Domain Knowledge for Question Answering](https://mlsanity.com/paper/2109.10547/) (2021)
- [Introducing Brain-like Concepts to Embodied Hand-crafted Dialog Management System](https://mlsanity.com/paper/2406.08996/) (2024)
- [Convex Polytope Modelling for Unsupervised Derivation of Semantic Structure for Data-efficient Natural Language Understanding](https://mlsanity.com/paper/2201.10588/) (2022)
- [A Low-Cost, Controllable and Interpretable Task-Oriented Chatbot: With Real-World After-Sale Services as Example](https://mlsanity.com/paper/2205.06436/) (2022)
- [Query-Response Interactions by Multi-tasks in Semantic Search for Chatbot Candidate Retrieval](https://mlsanity.com/paper/2208.11018/) (2022)
- [Improve Retrieval-based Dialogue System via Syntax-Informed Attention](https://mlsanity.com/paper/2303.06605/) (2023)
- [Dialog-to-Actions: Building Task-Oriented Dialogue System via Action-Level Generation](https://mlsanity.com/paper/2304.00884/) (2023)
- [Learning from Online User Feedback for Shopping Agents](https://mlsanity.com/paper/2608.11604/) (2026)
- [Precognition in Task-oriented Dialogue Understanding: Posterior Regularization by Future Context](https://mlsanity.com/paper/2203.03244/) (2022)
- [Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension](https://mlsanity.com/paper/2301.03953/) (2023)
- [Modeling shopper interest broadness with entropy-driven dialogue policy in the context of arbitrarily large product catalogs](https://mlsanity.com/paper/2509.06185/) (2025)
- [Learning to Detect Relevant Contexts and Knowledge for Response Selection in Retrieval-based Dialogue Systems](https://mlsanity.com/paper/2509.22845/) (2025)
- [DTKG: Dual-Track Knowledge Graph-Verified Reasoning Framework for Multi-Hop QA](https://mlsanity.com/paper/2510.16302/) (2025)

## Abstract (arXiv)

We present AliMe Assist, an intelligent assistant designed for creating an innovative online shopping experience in E-commerce. Based on question answering (QA), AliMe Assist offers assistance service, customer service, and chatting service. It is able to take voice and text input, incorporate context to QA, and support multi-round interaction. Currently, it serves millions of customer questions per day and is able to address 85% of them. In this paper, we demonstrate the system, present the underlying techniques, and share our experience in dealing with real-world QA in the E-commerce field.