4.8k citations · 7k across the 47 of their papers we have counts for
12 papers · 1 filter
Teach LLMs to Personalize -- An Approach inspired by Writing Education
Cheng Li, Mingyang Zhang, Qiaozhu Mei +4
Personalized text generation is an emerging research area that has attracted much attention in recent years. Most studies in this direction focus on a particular domain by designin…
Why is constrained neural language generation particularly challenging?
Cristina Garbacea, Qiaozhu Mei
Recent advances in deep neural language models combined with the capacity of large scale datasets have accelerated the development of natural language generation systems that produ…
Neural Language Generation: Formulation, Methods, and Evaluation
Cristina Garbacea, Qiaozhu Mei
Recent advances in neural network-based generative modeling have reignited the hopes in having computer systems capable of seamlessly conversing with humans and able to understand…
Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification
Cristina Garbacea, Mengtian Guo, Samuel Carton +1
Text simplification reduces the language complexity of professional content for accessibility purposes. End-to-end neural network models have been widely adopted to directly genera…
Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation
Cristina Garbacea, Samuel Carton, Shiyan Yan +1
We conduct a large-scale, systematic study to evaluate the existing evaluation methods for natural language generation in the context of generating online product reviews. We compa…
Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts
Samuel Carton, Qiaozhu Mei, Paul Resnick
We introduce an adversarial method for producing high-recall explanations of neural text classifier decisions. Building on an existing architecture for extractive explanations via…
Find the Conversation Killers: a Predictive Study of Thread-ending Posts
Yunhao Jiao, Cheng Li, Fei Wu +1
How to improve the quality of conversations in online communities has attracted considerable attention recently. Having engaged, urbane, and reactive online conversations has a cri…
End-to-end Learning for Short Text Expansion
Jian Tang, Yue Wang, Kai Zheng +1
Effectively making sense of short texts is a critical task for many real world applications such as search engines, social media services, and recommender systems. The task is part…
Deep Memory Networks for Attitude Identification
Cheng Li, Xiaoxiao Guo, Qiaozhu Mei
We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that i…
Context-aware Natural Language Generation with Recurrent Neural Networks
Jian Tang, Yifan Yang, Sam Carton +2
This paper studied generating natural languages at particular contexts or situations. We proposed two novel approaches which encode the contexts into a continuous semantic represen…
Identity-sensitive Word Embedding through Heterogeneous Networks
Jian Tang, Meng Qu, Qiaozhu Mei
Most existing word embedding approaches do not distinguish the same words in different contexts, therefore ignoring their contextual meanings. As a result, the learned embeddings o…
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
Jian Tang, Meng Qu, Qiaozhu Mei
Unsupervised text embedding methods, such as Skip-gram and Paragraph Vector, have been attracting increasing attention due to their simplicity, scalability, and effectiveness. Howe…