activity
20192024
most citedGuided Generation of Cause and Effect

52 citations · 77 across the 5 of their papers we have counts for

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Showing cs.CLShow all

5 papers · 1 filter

cs.CL20241 cited

Causal-Guided Active Learning for Debiasing Large Language Models

Li Du, Zhouhao Sun, Xiao Ding +5

Although achieving promising performance, recent analyses show that current generative large language models (LLMs) may still capture dataset biases and utilize them for generation…

cs.CL202113 cited

CausalBERT: Injecting Causal Knowledge Into Pre-trained Models with Minimal Supervision

Zhongyang Li, Xiao Ding, Kuo Liao +2

Recent work has shown success in incorporating pre-trained models like BERT to improve NLP systems. However, existing pre-trained models lack of causal knowledge which prevents tod…

cs.CL202152 cited

Guided Generation of Cause and Effect

Zhongyang Li, Xiao Ding, Ting Liu +2

We present a conditional text generation framework that posits sentential expressions of possible causes and effects. This framework depends on two novel resources we develop in th…

cs.CL2019

Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder

Li Du, Xiao Ding, Ting Liu +1

Understanding event and event-centered commonsense reasoning are crucial for natural language processing (NLP). Given an observed event, it is trivial for human to infer its intent…

cs.CL201911 cited

Story Ending Prediction by Transferable BERT

Zhongyang Li, Xiao Ding, Ting Liu

Recent advances, such as GPT and BERT, have shown success in incorporating a pre-trained transformer language model and fine-tuning operation to improve downstream NLP systems. How…