124 citations · 260 across the 7 of their papers we have counts for
10 papers
Text Characterization Toolkit
Daniel Simig, Tianlu Wang, Verna Dankers +4
In NLP, models are usually evaluated by reporting single-number performance scores on a number of readily available benchmarks, without much deeper analysis. Here, we argue that -…
Selective Annotation Makes Language Models Better Few-Shot Learners
Hongjin Su, Jungo Kasai, Chen Henry Wu +8
Many recent approaches to natural language tasks are built on the remarkable abilities of large language models. Large language models can perform in-context learning, where they l…
General Multi-label Image Classification with Transformers
Jack Lanchantin, Tianlu Wang, Vicente Ordonez +1
Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image. In this work we propose the C…
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation
Tianlu Wang, Xuezhi Wang, Yao Qin +5
NLP models are shown to suffer from robustness issues, i.e., a model's prediction can be easily changed under small perturbations to the input. In this work, we present a Controlle…
Visual News: Benchmark and Challenges in News Image Captioning
Fuxiao Liu, Yinghan Wang, Tianlu Wang +1
We propose Visual News Captioner, an entity-aware model for the task of news image captioning. We also introduce Visual News, a large-scale benchmark consisting of more than one mi…
Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation
Tianlu Wang, Xi Victoria Lin, Nazneen Fatema Rajani +3
Word embeddings derived from human-generated corpora inherit strong gender bias which can be further amplified by downstream models. Some commonly adopted debiasing approaches, inc…