Publications (16)
An Empirical Study of Content Understanding in Conversational Question Answering
Ting-Rui Chiang, Hao-Tong Ye, Yun-Nung Chen
With a lot of work about context-free question answering systems, there is an emerging trend of conversational question answering models in the natural language processing field. T…
The Distributional Hypothesis Does Not Fully Explain the Benefits of Masked Language Model Pretraining
Ting-Rui Chiang, Dani Yogatama
We analyze the masked language modeling pretraining objective function from the perspective of the distributional hypothesis. We investigate whether better sample efficiency and th…
Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems
Ting-Rui Chiang, Yun-Nung Chen
Solving math word problems is a challenging task that requires accurate natural language understanding to bridge natural language texts and math expressions. Motivated by the intui…
On Retrieval Augmentation and the Limitations of Language Model Training
Ting-Rui Chiang, Xinyan Velocity Yu, Joshua Robinson +3
Augmenting a language model (LM) with -nearest neighbors (NN) retrieval on its training data alone can decrease its perplexity, though the underlying reasons for this remain…
RAP-Net: Recurrent Attention Pooling Networks for Dialogue Response Selection
Chao-Wei Huang, Ting-Rui Chiang, Shang-Yu Su +1
The response selection has been an emerging research topic due to the growing interest in dialogue modeling, where the goal of the task is to select an appropriate response for con…
Pelican Soup Framework: A Theoretical Framework for Language Model Capabilities
Ting-Rui Chiang, Dani Yogatama
In this work, we propose a simple theoretical framework, Pelican Soup, aiming to better understand how pretraining allows LLMs to (1) generalize to unseen instructions and (2) perf…