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Found 6 papers, 2 papers with code
Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild?
Task-oriented dialogue (TOD) systems are mainly based on the slot-filling-based TOD (SF-TOD) framework, in which dialogues are broken down into smaller, controllable units (i. e., slots) to fulfill a specific task.
Keep Me Updated! Memory Management in Long-term Conversations
Remembering important information from the past and continuing to talk about it in the present are crucial in long-term conversations.
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models
In this work, we study the challenge of imposing roles on open-domain dialogue systems, with the goal of making the systems maintain consistent roles while conversing naturally with humans.
On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model
Many recent studies on large-scale language models have reported successful in-context zero- and few-shot learning ability.
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers
GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data.
Convergence Analysis of Optimization Algorithms
The regret bound of an optimization algorithms is one of the basic criteria for evaluating the performance of the given algorithm.
