151 citations · 498 across the 65 of their papers we have counts for
45 papers · 1 filter
Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute Manipulation
Letian Peng, Yuwei Zhang, Jingbo Shang
Prompting large language models (LLMs) for data augmentation has recently become a common practice in few-shot NLP tasks. In this paper, we propose Chain-of-Thought Attribute Manip…
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction
Hejie Cui, Rongmei Lin, Nasser Zalmout +4
Information extraction, e.g., attribute value extraction, has been extensively studied and formulated based only on text. However, many attributes can benefit from image-based extr…
SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to Rank
Dheeraj Mekala, Adithya Samavedhi, Chengyu Dong +1
Deep neural classifiers trained with cross-entropy loss (CE loss) often suffer from poor calibration, necessitating the task of out-of-distribution (OOD) detection. Traditional sup…
Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path
Zilong Wang, Jingbo Shang
The rapid growth of web pages and the increasing complexity of their structure poses a challenge for web mining models. Web mining models are required to understand the semi-struct…
A Benchmark on Extremely Weakly Supervised Text Classification: Reconcile Seed Matching and Prompting Approaches
Zihan Wang, Tianle Wang, Dheeraj Mekala +1
Etremely Weakly Supervised Text Classification (XWS-TC) refers to text classification based on minimal high-level human guidance, such as a few label-indicative seed words or class…
ClusterLLM: Large Language Models as a Guide for Text Clustering
Yuwei Zhang, Zihan Wang, Jingbo Shang
We introduce ClusterLLM, a novel text clustering framework that leverages feedback from an instruction-tuned large language model, such as ChatGPT. Compared with traditional unsupe…
Towards Few-shot Entity Recognition in Document Images: A Graph Neural Network Approach Robust to Image Manipulation
Prashant Krishnan, Zilong Wang, Yangkun Wang +1
Recent advances of incorporating layout information, typically bounding box coordinates, into pre-trained language models have achieved significant performance in entity recognitio…
Debiasing Made State-of-the-art: Revisiting the Simple Seed-based Weak Supervision for Text Classification
Chengyu Dong, Zihan Wang, Jingbo Shang
Recent advances in weakly supervised text classification mostly focus on designing sophisticated methods to turn high-level human heuristics into quality pseudo-labels. In this pap…
Goal-Driven Explainable Clustering via Language Descriptions
Zihan Wang, Jingbo Shang, Ruiqi Zhong
Unsupervised clustering is widely used to explore large corpora, but existing formulations neither consider the users' goals nor explain clusters' meanings. We propose a new task f…
Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting
William Hogan, Jiacheng Li, Jingbo Shang
Open-world Relation Extraction (OpenRE) has recently garnered significant attention. However, existing approaches tend to oversimplify the problem by assuming that all unlabeled te…
WOT-Class: Weakly Supervised Open-world Text Classification
Tianle Wang, Zihan Wang, Weitang Liu +1
State-of-the-art weakly supervised text classification methods, while significantly reduced the required human supervision, still requires the supervision to cover all the classes…
Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification
Zi Lin, Jeremiah Liu, Jingbo Shang
Pre-trained seq2seq models excel at graph semantic parsing with rich annotated data, but generalize worse to out-of-distribution (OOD) and long-tail examples. In comparison, symbol…
Progressive Sentiment Analysis for Code-Switched Text Data
Sudhanshu Ranjan, Dheeraj Mekala, Jingbo Shang
Multilingual transformer language models have recently attracted much attention from researchers and are used in cross-lingual transfer learning for many NLP tasks such as text cla…
Leveraging QA Datasets to Improve Generative Data Augmentation
Dheeraj Mekala, Tu Vu, Timo Schick +1
The ability of generative language models (GLMs) to generate text has improved considerably in the last few years, enabling their use for generative data augmentation. In this work…
LOPS: Learning Order Inspired Pseudo-Label Selection for Weakly Supervised Text Classification
Dheeraj Mekala, Chengyu Dong, Jingbo Shang
Weakly supervised text classification methods typically train a deep neural classifier based on pseudo-labels. The quality of pseudo-labels is crucial to final performance but they…
Fine-grained Contrastive Learning for Relation Extraction
William Hogan, Jiacheng Li, Jingbo Shang
Recent relation extraction (RE) works have shown encouraging improvements by conducting contrastive learning on silver labels generated by distant supervision before fine-tuning on…
WeDef: Weakly Supervised Backdoor Defense for Text Classification
Lesheng Jin, Zihan Wang, Jingbo Shang
Existing backdoor defense methods are only effective for limited trigger types. To defend different trigger types at once, we start from the class-irrelevant nature of the poisonin…
Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognition
Zihan Wang, Kewen Zhao, Zilong Wang +1
Fine-tuning pre-trained language models has recently become a common practice in building NLP models for various tasks, especially few-shot tasks. We argue that under the few-shot…
OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision
Xinyang Zhang, Chenwei Zhang, Xian Li +4
Automatic extraction of product attributes from their textual descriptions is essential for online shopper experience. One inherent challenge of this task is the emerging nature of…
Towards Few-shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework
Zilong Wang, Jingbo Shang
Entity recognition is a fundamental task in understanding document images. Traditional sequence labeling frameworks treat the entity types as class IDs and rely on extensive data a…
UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining
Jiacheng Li, Jingbo Shang, Julian McAuley
High-quality phrase representations are essential to finding topics and related terms in documents (a.k.a. topic mining). Existing phrase representation learning methods either sim…
Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data
Dheeraj Mekala, Varun Gangal, Jingbo Shang
Existing text classification methods mainly focus on a fixed label set, whereas many real-world applications require extending to new fine-grained classes as the number of samples…
BFClass: A Backdoor-free Text Classification Framework
Zichao Li, Dheeraj Mekala, Chengyu Dong +1
Backdoor attack introduces artificial vulnerabilities into the model by poisoning a subset of the training data via injecting triggers and modifying labels. Various trigger design…
LayoutReader: Pre-training of Text and Layout for Reading Order Detection
Zilong Wang, Yiheng Xu, Lei Cui +2
Reading order detection is the cornerstone to understanding visually-rich documents (e.g., receipts and forms). Unfortunately, no existing work took advantage of advanced deep lear…