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About this paper
Oriol Vinyals, Quoc Le
A standard sequence-to-sequence neural network, trained on large conversational datasets, demonstrates its ability to generate coherent and contextually relevant responses for both open-domain and task-specific conversations. The model showed capabilities in implicit knowledge extraction and simple common sense reasoning, while also highlighting challenges in maintaining consistency and providing non-generic replies.
Key insights
- End-to-end sequence-to-sequence models can learn to generate surprisingly coherent and contextually relevant conversational responses from raw data.
- The model implicitly extracts some form of common sense and general knowledge from large, noisy conversational datasets.
- Despite its capabilities, a simple seq2seq model trained with a cross-entropy objective struggles with maintaining consistent personality, avoiding generic responses, and robust factual reasoning.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.