B. Rex: a dialogue agent for book recommendations

2019-09-01 · WS 2019 9 · Mitchell Abrams, Luke Gessler, Matthew Marge

We present B. Rex, a dialogue agent for book recommendations. B. Rex aims to exploit the cognitive ease of natural dialogue and the excitement of a whimsical persona in order to engage users who might not enjoy using more common interfaces for finding new books. B. Rex succeeds in making book recommendations with good quality based on only information revealed by the user in the dialogue.

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Semantics-Enhanced Task-Oriented Dialogue Translation: A Case Study on Hotel Booking

2017-11-01 · IJCNLP 2017 11 · Long-Yue Wang, Jinhua Du, Liangyou Li, Zhaopeng Tu 외

We showcase TODAY, a semantics-enhanced task-oriented dialogue translation system, whose novelties are: (i) task-oriented named entity (NE) definition and a hybrid strategy for NE recognition and translation; and (ii) a …

Dialogue Understanding Machine Translation Management Named Entity Recognition (NER)+1

AirDialogue: An Environment for Goal-Oriented Dialogue Research

2018-10-01 · EMNLP 2018 10 · Wei Wei, Quoc Le, Andrew Dai, Jia Li

Recent progress in dialogue generation has inspired a number of studies on dialogue systems that are capable of accomplishing tasks through natural language interactions. A promising direction among these studies is the …

Dialogue Generation Reinforcement Learning Text Generation

A User Simulator for Task-Completion Dialogues

2016-12-17 · Xiujun Li, Zachary C. Lipton, Bhuwan Dhingra, Lihong Li 외

Despite widespread interests in reinforcement-learning for task-oriented dialogue systems, several obstacles can frustrate research and development progress. First, reinforcement learners typically require interaction wi…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Task-Oriented Dialogue Systems+1

Which books do I like?

2025-03-05 · Hannes Rosenbusch, Erdem Ozan Meral

Finding enjoyable fiction books can be challenging, partly because stories are multi-faceted and one's own literary taste might be difficult to ascertain. Here, we introduce the ISAAC method (Introspection-Support, AI-An…

AI Agent

Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning

2017-10-31 · Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu 외

This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems. Inspired by generati…

Task-Completion Dialogue Policy Learning