Training Millions of Personalized Dialogue Agents
Autor: | Martin Raison, Samuel Humeau, Antoine Bordes, Pierre-Emmanuel Mazaré |
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Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Computer science 02 engineering and technology Persona 010501 environmental sciences 01 natural sciences World Wide Web InformationSystems_MODELSANDPRINCIPLES Scale (social sciences) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computation and Language (cs.CL) 0105 earth and related environmental sciences |
Zdroj: | EMNLP |
Popis: | Current dialogue systems are not very engaging for users, especially when trained end-to-end without relying on proactive reengaging scripted strategies. Zhang et al. (2018) showed that the engagement level of end-to-end dialogue models increases when conditioning them on text personas providing some personalized back-story to the model. However, the dataset used in Zhang et al. (2018) is synthetic and of limited size as it contains around 1k different personas. In this paper we introduce a new dataset providing 5 million personas and 700 million persona-based dialogues. Our experiments show that, at this scale, training using personas still improves the performance of end-to-end systems. In addition, we show that other tasks benefit from the wide coverage of our dataset by fine-tuning our model on the data from Zhang et al. (2018) and achieving state-of-the-art results. EMNLP 2018 |
Databáze: | OpenAIRE |
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