Target Guided Emotion Aware Chat Machine
arXiv:2011.07432
Abstract
The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge is not well addressed in the literature, since most of the approaches neglect the emotional information conveyed by a post while generating responses. This article addresses this problem by proposing a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post and leverage target information for generating more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness.
arXiv commentTo appear on TOIS 2021
References in corpus (17)
- Sequence to Sequence Learning with Neural Networks
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- A Neural Conversational Model
- A Structured Self-attentive Sentence Embedding
- Convolutional Neural Network Architectures for Matching Natural Language Sentences
- Effective Approaches to Attention-based Neural Machine Translation
- Topic Aware Neural Response Generation
- A Diversity-Promoting Objective Function for Neural Conversation Models
- A Simple, Fast Diverse Decoding Algorithm for Neural Generation
- Neural Responding Machine for Short-Text Conversation
- An Information Retrieval Approach to Short Text Conversation
- A Persona-Based Neural Conversation Model
- Toward Controlled Generation of Text
- On Using Very Large Target Vocabulary for Neural Machine Translation
- Assigning personality/identity to a chatting machine for coherent conversation generation
- MojiTalk: Generating Emotional Responses at Scale
- Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots