Learning Personalized End-to-End Goal-Oriented Dialog

By Liangchen Luo, Wen-Fen Huang, Qi Zeng, Zaiqing Nie, Xu Sun. 2018

87 citations recorded by the source corpus.

Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues. In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs. We first introduce a PROFILE MODEL which encodes user profiles into distributed embeddings and refers to conversation history from other similar users. Then a PREFERENCE MODEL captures user preferences over knowledge base entities to handle the ambiguity in user requests. The two models are combined into the PERSONALIZED MEMN2N. Experiments show that the proposed model achieves qualitative performance improvements over state-of-the-art methods. As for human evaluation, it also outperforms other approaches in terms of task completion rate and user satisfaction.

Subfields: Artificial Intelligence, Dialogue & Conversational AI

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Citations in the corpus

Learning from Dialogue after Deployment: Feed Yourself, Chatbot! (2019, background): “Since the question of how to incorporate external knowledge (such as profiles) in dialogue is an open research question of its own (Li et al., 2016; Luan et al., 2017; Luo et al., 2018) and we are primarily interested in the question of learning from dialogue, we discard the profiles and simply trai”
Lifelong and Continual Learning Dialogue Systems: Learning during Conversation (2021, background): “Some related work include (Luo et al. 2019; Liu et al. 2020).”
Adaptive Gradient Methods with Dynamic Bound of Learning Rate (2019, methodology): “Indeed, the optimizer is chosen as S GD (or with momentum) in several recent state-of-the-art works in natural language processing and computer vision (Luo et al., 2019; Wu & He, 2018), wherein these instances S GD does perform better than adaptive methods.”
Condition Aware and Revise Transformer for Question Answering (2020, methodology): “[19] use an end-to-endmemory network to select response in candidate sets.”
You Impress Me: Dialogue Generation via Mutual Persona Perception (2020, background): “Personalization in goal-oriented dialogue systems has also received some attention (Joshi et al., 2017; Luo et al., 2019).”