38 citations · 65 across the 11 of their papers we have counts for
19 papers · 1 filter
On the Effectiveness of Parameter-Efficient Fine-Tuning
Zihao Fu, Haoran Yang, Anthony Man-Cho So +3
Fine-tuning pre-trained models has been ubiquitously proven to be effective in a wide range of NLP tasks. However, fine-tuning the whole model is parameter inefficient as it always…
How to tackle an emerging topic? Combining strong and weak labels for Covid news NER
Aleksander Ficek, Fangyu Liu, Nigel Collier
Being able to train Named Entity Recognition (NER) models for emerging topics is crucial for many real-world applications especially in the medical domain where new topics are cont…
Do ever larger octopi still amplify reporting biases? Evidence from judgments of typical colour
Fangyu Liu, Julian Martin Eisenschlos, Jeremy R. Cole +1
Language models (LMs) trained on raw texts have no direct access to the physical world. Gordon and Van Durme (2013) point out that LMs can thus suffer from reporting bias: texts ra…
Visually Grounded Reasoning across Languages and Cultures
Fangyu Liu, Emanuele Bugliarello, Edoardo Maria Ponti +3
The design of widespread vision-and-language datasets and pre-trained encoders directly adopts, or draws inspiration from, the concepts and images of ImageNet. While one can hardly…
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models
Qianchu Liu, Fangyu Liu, Nigel Collier +2
Recent work indicated that pretrained language models (PLMs) such as BERT and RoBERTa can be transformed into effective sentence and word encoders even via simple self-supervised t…
Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT
Zaiqiao Meng, Fangyu Liu, Thomas Hikaru Clark +2
Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks. In this paper, we proposed Mixture-of-Partitions (MoP), an infusion approach t…