3 papers
cs.CL2024
An Empirical Study on Context Length for Open-Domain Dialog Generation
Xinyi Shen, Zuoquan Lin
Transformer-based open-domain dialog models have become increasingly popular in recent years. These models typically represent context as a concatenation of a dialog history. Howev…
cs.LG2024
Incorporating Exponential Smoothing into MLP: A Simple but Effective Sequence Model
Jiqun Chu, Zuoquan Lin
Modeling long-range dependencies in sequential data is a crucial step in sequence learning. A recently developed model, the Structured State Space (S4), demonstrated significant ef…
cs.CL2024
Local and Global Contexts for Conversation
Zuoquan Lin, Xinyi Shen
The context in conversation is the dialog history crucial for multi-turn dialogue. Learning from the relevant contexts in dialog history for grounded conversation is a challenging…