Jinlan Fu

RethinkCWS: Is Chinese Word Segmentation a Solved Task?

2020

The performance of the Chinese Word Segmentation (CWS) systems has gradually reached a plateau with the rapid development of deep neural networks, especially the successful use of large pre-trained models. In this paper, we take …

ExplainaBoard: An Explainable Leaderboard for NLP

2021 · arXiv (Cornell University)

With the rapid development of NLP research, leaderboards have emerged as one tool to track the performance of various systems on various NLP tasks. They are effective in this goal to some extent, but generally …

MCM-DPO: Multifaceted Cross-Modal Direct Preference Optimization for Alt-text Generation

2025 · arXiv (Cornell University)

The alt-text generation task produces concise, context-relevant descriptions of images, enabling blind and low-vision users to access online images. Despite the capabilities of large vision-language models, alt-text generation performance remains limited due to noisy user …

Adaptive Co-attention Network for Named Entity Recognition in Tweets

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

In this study, we investigate the problem of named entity recognition for tweets. Named entity recognition is an important task in natural language processing and has been carefully studied in recent decades. Previous named entity …

A Lexicon-Based Graph Neural Network for Chinese NER

2019

Tao Gui, Yicheng Zou, Qi Zhang, Minlong Peng, Jinlan Fu, Zhongyu Wei, Xuanjing Huang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language …

Interpretable Multi-dataset Evaluation for Named Entity Recognition

2020

With the proliferation of models for natural language processing tasks, it is even harder to understand the differences between models and their relative merits. Simply looking at differences between holistic metrics such as accuracy, BLEU, …

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

2022 · ACM Computing Surveys

This article surveys and organizes research works in a new paradigm in natural language processing, which we dub “prompt-based learning.” Unlike traditional supervised learning, which trains a model to take in an input x and …

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

2021 · arXiv (Cornell University)

This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub "prompt-based learning". Unlike traditional supervised learning, which trains a model to take in an input x and …