Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
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Abstract
We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.
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The record
- Venue
- Topic
- Topic Modeling
- Field
- Computer Science
- Canadian institutions
- McGill UniversityUniversité de Montréal
- Funders
- Compute CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanadian Institute for Advanced Research
- Keywords
- Generative grammarComputer scienceBootstrapping (finance)Word (group theory)Artificial intelligenceEnd-to-end principleArtificial neural networkLanguage modelDomain (mathematical analysis)EncoderRecurrent neural networkTask (project management)Natural language processingGenerative modelLinguistics
- Has abstract in OpenAlex
- yes