113 citations · 454 across the 27 of their papers we have counts for
15 papers · 1 filter
Lifelong and Continual Learning Dialogue Systems
Sahisnu Mazumder, Bing Liu
Dialogue systems, commonly known as chatbots, have gained escalating popularity in recent times due to their wide-spread applications in carrying out chit-chat conversations with u…
Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings
Jorge A. Mendez, Alborz Geramifard, Mohammad Ghavamzadeh +1
Learning task-oriented dialog policies via reinforcement learning typically requires large amounts of interaction with users, which in practice renders such methods unusable for re…
KETOD: Knowledge-Enriched Task-Oriented Dialogue
Zhiyu Chen, Bing Liu, Seungwhan Moon +3
Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. Towards building a human-like assistant that can converse natura…
User Memory Reasoning for Conversational Recommendation
Hu Xu, Seungwhan Moon, Honglei Liu +3
We study a conversational recommendation model which dynamically manages users' past (offline) preferences and current (online) requests through a structured and cumulative user me…
Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models
Tianxing He, Jun Liu, Kyunghyun Cho +4
In this work, we study how the finetuning stage in the pretrain-finetune framework changes the behavior of a pretrained neural language generator. We focus on the transformer encod…
Adversarial Learning of Task-Oriented Neural Dialog Models
Bing Liu, Ian Lane
In this work, we propose an adversarial learning method for reward estimation in reinforcement learning (RL) based task-oriented dialog models. Most of the current RL based task-or…
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems
Bing Liu, Gokhan Tur, Dilek Hakkani-Tur +2
In this work, we present a hybrid learning method for training task-oriented dialogue systems through online user interactions. Popular methods for learning task-oriented dialogues…
Multi-Domain Adversarial Learning for Slot Filling in Spoken Language Understanding
Bing Liu, Ian Lane
The goal of this paper is to learn cross-domain representations for slot filling task in spoken language understanding (SLU). Most of the recently published SLU models are domain-s…
End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning
Bing Liu, Gokhan Tur, Dilek Hakkani-Tur +2
In this paper, we present a neural network based task-oriented dialogue system that can be optimized end-to-end with deep reinforcement learning (RL). The system is able to track d…
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models
Bing Liu, Tong Yu, Ian Lane +1
Dialog response selection is an important step towards natural response generation in conversational agents. Existing work on neural conversational models mainly focuses on offline…
Iterative Policy Learning in End-to-End Trainable Task-Oriented Neural Dialog Models
Bing Liu, Ian Lane
In this paper, we present a deep reinforcement learning (RL) framework for iterative dialog policy optimization in end-to-end task-oriented dialog systems. Popular approaches in le…
An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog
Bing Liu, Ian Lane
We present a novel end-to-end trainable neural network model for task-oriented dialog systems. The model is able to track dialog state, issue API calls to knowledge base (KB), and…
Dialog Context Language Modeling with Recurrent Neural Networks
Bing Liu, Ian Lane
In this work, we propose contextual language models that incorporate dialog level discourse information into language modeling. Previous works on contextual language model treat pr…
Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks
Bing Liu, Ian Lane
Speaker intent detection and semantic slot filling are two critical tasks in spoken language understanding (SLU) for dialogue systems. In this paper, we describe a recurrent neural…
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
Bing Liu, Ian Lane
Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition. In this work, we propose an attention-bas…