Alexandros Papangelis

Also published as: Alex Papangelis


2020

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Controllable Text Generation with Focused Variation
Lei Shu | Alexandros Papangelis | Yi-Chia Wang | Gokhan Tur | Hu Xu | Zhaleh Feizollahi | Bing Liu | Piero Molino
Findings of the Association for Computational Linguistics: EMNLP 2020

This work introduces Focused-Variation Network (FVN), a novel model to control language generation. The main problems in previous controlled language generation models range from the difficulty of generating text according to the given attributes, to the lack of diversity of the generated texts. FVN addresses these issues by learning disjoint discrete latent spaces for each attribute inside codebooks, which allows for both controllability and diversity, while at the same time generating fluent text. We evaluate FVN on two text generation datasets with annotated content and style, and show state-of-the-art performance as assessed by automatic and human evaluations.

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Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI
Tsung-Hsien Wen | Asli Celikyilmaz | Zhou Yu | Alexandros Papangelis | Mihail Eric | Anuj Kumar | Iñigo Casanueva | Rushin Shah
Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI

2019

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Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
Satoshi Nakamura | Milica Gasic | Ingrid Zuckerman | Gabriel Skantze | Mikio Nakano | Alexandros Papangelis | Stefan Ultes | Koichiro Yoshino
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

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Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning
Alexandros Papangelis | Yi-Chia Wang | Piero Molino | Gokhan Tur
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

Some of the major challenges in training conversational agents include the lack of large-scale data of real-world complexity, defining appropriate evaluation measures, and managing meaningful conversations across many topics over long periods of time. Moreover, most works tend to assume that the conversational agent’s environment is stationary, a somewhat strong assumption. To remove this assumption and overcome the lack of data, we take a step away from the traditional training pipeline and model the conversation as a stochastic collaborative game. Each agent (player) has a role (“assistant”, “tourist”, “eater”, etc.) and their own objectives, and can only interact via language they generate. Each agent, therefore, needs to learn to operate optimally in an environment with multiple sources of uncertainty (its own LU and LG, the other agent’s LU, Policy, and LG). In this work, we present the first complete attempt at concurrently training conversational agents that communicate only via self-generated language and show that they outperform supervised and deep learning baselines.

2018

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Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
Kazunori Komatani | Diane Litman | Kai Yu | Alex Papangelis | Lawrence Cavedon | Mikio Nakano
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

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Spoken Dialogue for Information Navigation
Alexandros Papangelis | Panagiotis Papadakos | Yannis Stylianou | Yannis Tzitzikas
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

Aiming to expand the current research paradigm for training conversational AI agents that can address real-world challenges, we take a step away from traditional slot-filling goal-oriented spoken dialogue systems (SDS) and model the dialogue in a way that allows users to be more expressive in describing their needs. The goal is to help users make informed decisions rather than being fed matching items. To this end, we describe the Linked-Data SDS (LD-SDS), a system that exploits semantic knowledge bases that connect to linked data, and supports complex constraints and preferences. We describe the required changes in language understanding and state tracking, and the need for mined features, and we report the promising results (in terms of semantic errors, effort, etc) of a preliminary evaluation after training two statistical dialogue managers in various conditions.

2016

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Special Session - The Future Directions of Dialogue-Based Intelligent Personal Assistants
Yoichi Matsuyama | Alexandros Papangelis
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2015

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Reinforcement Learning of Multi-Issue Negotiation Dialogue Policies
Alexandros Papangelis | Kallirroi Georgila
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2012

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Evaluation of Online Dialogue Policy Learning Techniques
Alexandros Papangelis | Vangelis Karkaletsis | Fillia Makedon
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

The number of applied Dialogue Systems is ever increasing in several service providing and other applications as a way to efficiently and inexpensively serve large numbers of customers. A DS that employs some form of adaptation to the environment and its users is called an Adaptive Dialogue System (ADS). A significant part of the research community has lately focused on ADS and many existing or novel techniques are being applied to this problem. One of the most promising techniques is Reinforcement Learning (RL) and especially online RL. This paper focuses on online RL techniques used to achieve adaptation in Dialogue Management and provides an evaluation of various such methods in an effort to aid the designers of ADS in deciding which method to use. To the best of our knowledge there is no other work to compare online RL techniques on the dialogue management problem.

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A Comparative Study of Reinforcement Learning Techniques on Dialogue Management
Alexandros Papangelis
Proceedings of the Student Research Workshop at the 13th Conference of the European Chapter of the Association for Computational Linguistics