Nayeon Lee


2020

pdf bib
Language Models as Fact Checkers?
Nayeon Lee | Belinda Li | Sinong Wang | Wen-tau Yih | Hao Ma | Madian Khabsa
Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)

Recent work has suggested that language models (LMs) store both common-sense and factual knowledge learned from pre-training data. In this paper, we leverage this implicit knowledge to create an effective end-to-end fact checker using a solely a language model, without any external knowledge or explicit retrieval components. While previous work on extracting knowledge from LMs have focused on the task of open-domain question answering, to the best of our knowledge, this is the first work to examine the use of language models as fact checkers. In a closed-book setting, we show that our zero-shot LM approach outperforms a random baseline on the standard FEVER task, and that our finetuned LM compares favorably with standard baselines. Though we do not ultimately outperform methods which use explicit knowledge bases, we believe our exploration shows that this method is viable and has much room for exploration.

2019

bib
Understanding the Shades of Sexism in Popular TV Series
Nayeon Lee | Yejin Bang | Jamin Shin | Pascale Fung
Proceedings of the 2019 Workshop on Widening NLP

[Multiple-submission] In the midst of a generation widely exposed to and influenced by media entertainment, the NLP research community has shown relatively little attention on the sexist comments in popular TV series. To understand sexism in TV series, we propose a way of collecting distant supervision dataset using Character Persona information with the psychological theories on sexism. We assume that sexist characters from TV shows are more prone to making sexist comments when talking about women, and show that this hypothesis is valid through experiment. Finally, we conduct an interesting analysis on popular TV show characters and successfully identify different shades of sexism that is often overlooked.

bib
Exploring Social Bias in Chatbots using Stereotype Knowledge
Nayeon Lee | Andrea Madotto | Pascale Fung
Proceedings of the 2019 Workshop on Widening NLP

Exploring social bias in chatbot is an important, yet relatively unexplored problem. In this paper, we propose an approach to understand social bias in chatbots by leveraging stereotype knowledge. It allows interesting comparison of bias between chatbots and humans, and provides intuitive analysis of existing chatbots by borrowing the finer-grain concepts of sexism and racism.

pdf bib
Team yeon-zi at SemEval-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled Data
Nayeon Lee | Zihan Liu | Pascale Fung
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes our system that has been submitted to SemEval-2019 Task 4: Hyperpartisan News Detection. We focus on removing the noise inherent in the hyperpartisanship dataset from both data-level and model-level by leveraging semi-supervised pseudo-labels and the state-of-the-art BERT model. Our model achieves 75.8% accuracy in the final by-article dataset without ensemble learning.

2018

pdf bib
Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging
Nayeon Lee | Chien-Sheng Wu | Pascale Fung
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Fact-checking of textual sources needs to effectively extract relevant information from large knowledge bases. In this paper, we extend an existing pipeline approach to better tackle this problem. We propose a neural ranker using a decomposable attention model that dynamically selects sentences to achieve promising improvement in evidence retrieval F1 by 38.80%, with (x65) speedup compared to a TF-IDF method. Moreover, we incorporate lexical tagging methods into our pipeline framework to simplify the tasks and render the model more generalizable. As a result, our framework achieves promising performance on a large-scale fact extraction and verification dataset with speedup.