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Found 11 papers, 5 papers with code
Bridging Philippine Languages With Multilingual Neural Machine Translation
The Philippines is home to more than 150 languages that is considered to be low-resourced even on its major languages.
Towards Automatic Construction of Filipino WordNet: Word Sense Induction and Synset Induction Using Sentence Embeddings
The resulting sense inventory and synonym sets can be used in automatically creating a wordnet.
Using Synthetic Data for Conversational Response Generation in Low-resource Settings
Lastly, we published the first Filipino conversational response generator capable of generating responses related to the previous 3 responses.
Improving Large-scale Language Models and Resources for Filipino
In this paper, we improve on existing language resources for the low-resource Filipino language in two ways.
Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets
Lastly, we perform analyses on transfer learning techniques to shed light on their true performance when operating in low-data domains through the use of degradation tests.
Establishing Baselines for Text Classification in Low-Resource Languages
We analyze our pretrained model's degradation speeds and look towards the use of this method for comparing models aimed at operating within the low-resource setting.
Simplifying Paragraph-level Question Generation via Transformer Language Models
Question generation (QG) is a natural language generation task where a model is trained to ask questions corresponding to some input text.
Annotation Process for the Dialog Act Classification of a Taglish E-commerce Q\&A Corpus
The SWBD-DAMSL tagset for DA classification was modified to 28 tags fitting the categories applicable to e-commerce conversations.
Localization of Fake News Detection via Multitask Transfer Learning
Second, we benchmark Transfer Learning (TL) techniques and show that they can be used to train robust fake news classifiers from little data, achieving 91% accuracy on our fake news dataset, reducing the error by 14% compared to established few-shot baselines.
Evaluating Language Model Finetuning Techniques for Low-resource Languages
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 apply state-of-the-art techniques directly.
Modeling Personality Traits of Filipino Twitter Users
Recent studies in the field of text-based personality recognition experiment with different languages, feature extraction techniques, and machine learning algorithms to create better and more accurate models; however, little focus is placed on exploring the language use of a group of individuals defined by nationality.
