activity
20192022
most citedEvaluating Language Model Finetuning Techniques for Low-resource Languages

32 citations · 68 across the 5 of their papers we have counts for

collaborators
Showing cs.CLShow all

5 papers · 1 filter

cs.CL20222 cited

Using Synthetic Data for Conversational Response Generation in Low-resource Settings

Gabriel Louis Tan, Adrian Paule Ty, Schuyler Ng +3

Response generation is a task in natural language processing (NLP) where a model is trained to respond to human statements. Conversational response generators take this one step fu…

cs.CL2020

Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets

Jan Christian Blaise Cruz, Jose Kristian Resabal, James Lin +2

Transformers represent the state-of-the-art in Natural Language Processing (NLP) in recent years, proving effective even in tasks done in low-resource languages. While pretrained t…

cs.CL202032 cited

Establishing Baselines for Text Classification in Low-Resource Languages

Jan Christian Blaise Cruz, Charibeth Cheng

While transformer-based finetuning techniques have proven effective in tasks that involve low-resource, low-data environments, a lack of properly established baselines and benchmar…

cs.CL2019

Localization of Fake News Detection via Multitask Transfer Learning

Jan Christian Blaise Cruz, Julianne Agatha Tan, Charibeth Cheng

The use of the internet as a fast medium of spreading fake news reinforces the need for computational tools that combat it. Techniques that train fake news classifiers exist, but t…

cs.CL201932 cited

Evaluating Language Model Finetuning Techniques for Low-resource Languages

Jan Christian Blaise Cruz, Charibeth Cheng

Unlike mainstream languages (such as English and French), low-resource languages often suffer from a lack of expert-annotated corpora and benchmark resources that make it hard to a…