Yen-Chun Chen

Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension

2019 · arXiv (Cornell University)

Multi-hop reading comprehension requires the model to explore and connect relevant information from multiple sentences/documents in order to answer the question about the context. To achieve this, we propose an interpretable 3-module system called Explore-Propose-Assemble …

Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Search

2024 · arXiv (Cornell University)

Programmatic reinforcement learning (PRL) has been explored for representing policies through programs as a means to achieve interpretability and generalization. Despite promising outcomes, current state-of-the-art PRL methods are hindered by sample inefficiency, necessitating tens of …

Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting

2018

Inspired by how humans summarize long documents, we propose an accurate and fast summarization model that first selects salient sentences and then rewrites them abstractively (i.e., compresses and paraphrases) to generate a concise overall summary. …

DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation

2020

Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 2020.

DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation

2019 · arXiv (Cornell University)

We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends …