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Found 13 papers, 5 papers with code
Data Processing Matters: SRPH-Konvergen AI’s Machine Translation System for WMT’21
In this paper, we describe the submission of the joint Samsung Research Philippines-Konvergen AI team for the WMT’21 Large Scale Multilingual Translation Task - Small Track 2.
Samsung R&D Institute Philippines at WMT 2023
In this paper, we describe the constrained MT systems submitted by Samsung R&D Institute Philippines to the WMT 2023 General Translation Task for two directions: en$\rightarrow$he and he$\rightarrow$en.
Multilingual Large Language Models Are Not (Yet) Code-Switchers
Multilingual Large Language Models (LLMs) have recently shown great capabilities in a wide range of tasks, exhibiting state-of-the-art performance through zero-shot or few-shot prompting methods.
Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages
While code-mixing is a common linguistic practice in many parts of the world, collecting high-quality and low-cost code-mixed data remains a challenge for natural language processing (NLP) research.
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.
Data Processing Matters: SRPH-Konvergen AI's Machine Translation System for WMT'21
In this paper, we describe the submission of the joint Samsung Research Philippines-Konvergen AI team for the WMT'21 Large Scale Multilingual Translation Task - Small Track 2.
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.
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.
