activity
20202022
most citedAutomatic Story Generation: Challenges and Attempts

7 citations · 15 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI20225 cited

NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty

Jonathan Balloch, Zhiyu Lin, Mustafa Hussain +5

A robust body of reinforcement learning techniques have been developed to solve complex sequential decision making problems. However, these methods assume that train and evaluation…

cs.CL20221 cited

XFBoost: Improving Text Generation with Controllable Decoders

Xiangyu Peng, Michael Sollami

Multimodal conditionality in transformer-based natural language models has demonstrated state-of-the-art performance in the task of product description generation. Recent approache…

cs.AI20212 cited

Detecting and Adapting to Novelty in Games

Xiangyu Peng, Jonathan C. Balloch, Mark O. Riedl

Open-world novelty occurs when the rules of an environment can change abruptly, such as when a game player encounters "house rules". To address open-world novelty, game playing age…

cs.CL20217 cited

Automatic Story Generation: Challenges and Attempts

Amal Alabdulkarim, Siyan Li, Xiangyu Peng

The scope of this survey paper is to explore the challenges in automatic story generation. We hope to contribute in the following ways: 1. Explore how previous research in story ge…

cs.CL2020

Reducing Non-Normative Text Generation from Language Models

Xiangyu Peng, Siyan Li, Spencer Frazier +1

Large-scale, transformer-based language models such as GPT-2 are pretrained on diverse corpora scraped from the internet. Consequently, they are prone to generating non-normative t…