Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce
arXiv:1806.05434
Abstract
Building multi-turn information-seeking conversation systems is an important and challenging research topic. Although several advanced neural text matching models have been proposed for this task, they are generally not efficient for industrial applications. Furthermore, they rely on a large amount of labeled data, which may not be available in real-world applications. To alleviate these problems, we study transfer learning for multi-turn information seeking conversations in this paper. We first propose an efficient and effective multi-turn conversation model based on convolutional neural networks. After that, we extend our model to adapt the knowledge learned from a resource-rich domain to enhance the performance. Finally, we deployed our model in an industrial chatbot called AliMe Assist (https://consumerservice.taobao.com/online-help) and observed a significant improvement over the existing online model.
arXiv comment6
References in corpus (13)
- Convolutional Neural Network Architectures for Matching Natural Language Sentences
- A large annotated corpus for learning natural language inference
- Unsupervised Cross-Domain Image Generation
- Deep Reinforcement Learning for Dialogue Generation
- Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
- Neural Responding Machine for Short-Text Conversation
- A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
- A Persona-Based Neural Conversation Model
- Text Matching as Image Recognition
- Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
- How Transferable are Neural Networks in NLP Applications?
- Adversarial Multi-Criteria Learning for Chinese Word Segmentation
- Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce