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MRF-Chat: Improving Dialogue with Markov Random Fields
Recent state-of-the-art approaches in open-domain dialogue include training end-to-end deep-learning models to learn various conversational features like emotional content of response, symbolic transitions of dialogue contexts in a knowledge graph and persona of the agent and the user, among others.
Combining pre-trained language models and structured knowledge
In recent years, transformer-based language models have achieved state of the art performance in various NLP benchmarks.
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