Browsing by Author "Ranathunga S"

Now showing 1 - 6 of 6
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    Dataset and Baseline for Automatic Student Feedback Analysis
    (European Language Resources Association (ELRA), 2022-01-01) Nilanga K; Herath M; Maduwantha H; Ranathunga S; Calzolari N; Béchet F; Blache P; Choukri K; Cieri C; Declerck T; Goggi S; Isahara H; Maegaard B; Mariani J; Mazo H; Odijk J; Piperidis S
    In this paper, we present a student feedback corpus that contains 3000 instances of feedback written by university students. This dataset has been annotated for aspect terms, opinion terms, polarities of the opinion terms towards targeted aspects, and document-level opinion polarities. We developed a hierarchical taxonomy for aspect categorisation, which covers many aspects of the teaching-learning process. We annotated both implicit and explicit aspects using this taxonomy. Annotation methodology, difficulties faced during the annotation, and the details of the aspect term categorization are discussed in detail. Using state-of-the-art techniques, we have built baseline models for the following tasks: Target oriented Opinion Extraction, Aspect Level Sentiment Analysis, and Document Level Sentiment Analysis. These models reported 64%, 75%, and 86% F1 scores (respectively) for the considered tasks. These results illustrate the reliability and usability of the corpus for different tasks related to sentiment analysis.
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    Lexicon-based fine-tuning of multilingual language models for low-resource language sentiment analysis
    (John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology., 2024-04-01) Dhananjaya V; Ranathunga S; Jayasena S
    Pre-trained multilingual language models (PMLMs) such as mBERT and XLM-R have shown good cross-lingual transferability. However, they are not specifically trained to capture cross-lingual signals concerning sentiment words. This poses a disadvantage for low-resource languages (LRLs) that are under-represented in these models. To better fine-tune these models for sentiment classification in LRLs, a novel intermediate task fine-tuning (ITFT) technique based on a sentiment lexicon of a high-resource language (HRL) is introduced. The authors experiment with LRLs Sinhala, Tamil and Bengali for a 3-class sentiment classification task and show that this method outperforms vanilla fine-tuning of the PMLM. It also outperforms or is on-par with basic ITFT that relies on an HRL sentiment classification dataset.
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    SiTSE: Sinhala Text Simplification Dataset and Evaluation
    (Association for Computing Machinery, 2025-05-08) Ranathunga S; Sirithunga R; Rathnayake H; De Silva L; Aluthwala T; Peramuna S; Shekhar R; Zitouni I
    Text Simplification is a task that has been minimally explored for low-resource languages. Consequently, there are only a few manually curated datasets. In this article, we present a human-curated sentence-level text simplification dataset for the Sinhala language. Our evaluation dataset contains 1,000 complex sentences and 3,000 corresponding simplified sentences produced by three different human annotators. We model the text simplification task as a zero-shot and zero-resource sequence-to-sequence (seq-seq) task on the multilingual language models mT5 and mBART. We exploit auxiliary data from related seq-seq tasks and explore the possibility of using intermediate task transfer learning (ITTL). Our analysis shows that ITTL outperforms the previously proposed zero-resource methods for text simplification. Our findings also highlight the challenges in evaluating text simplification systems and support the calls for improved metrics for measuring the quality of automated text simplification systems that would suit low-resource languages as well. Our code and data are publicly available: https://github.com/brainsharks-fyp17/Sinhala-Text-Simplification-Dataset-andEvaluation.
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    Transfer learning on transformers for building energy consumption forecasting—A comparative study
    (Elsevier B V, 2025-06-01) Spencer R; Ranathunga S; Boulic M; van Heerden AH; Susnjak T
    Energy consumption in buildings is steadily increasing, leading to higher carbon emissions. Predicting energy consumption is a key factor in addressing climate change. There has been a significant shift from traditional statistical models to advanced deep learning (DL) techniques for predicting energy use in buildings. However, data scarcity in newly constructed or poorly instrumented buildings limits the effectiveness of standard DL approaches. In this study, we investigate the application of six data-centric Transfer Learning (TL) strategies on three Transformer architectures—vanilla Transformer, Informer, and PatchTST—to enhance building energy consumption forecasting. Transformers, a relatively new DL framework, have demonstrated significant promise in various domains; yet, prior TL research has often focused on either a single data-centric strategy or older models such as Recurrent Neural Networks. Using 16 diverse datasets from the Building Data Genome Project 2, we conduct an extensive empirical analysis under varying feature spaces (e.g., recorded ambient weather) and building characteristics (e.g., dataset volume). Our experiments show that combining multiple source datasets under a zero-shot setup reduces the Mean Absolute Error (MAE) of the vanilla Transformer model by an average of 15.9 % for 24 h forecasts, compared to single-source baselines. Further fine-tuning these multi-source models with target-domain data yields an additional 3–5 % improvement. Notably, PatchTST outperforms the vanilla Transformer and Informer models. Overall, our results underscore the potential of combining Transformer architectures with TL techniques to enhance building energy consumption forecasting accuracy. However, careful selection of the TL strategy and attention to feature space compatibility are needed to maximize forecasting gains.
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    Use of prompt-based learning for code-mixed and code-switched text classification
    (Springer Nature, 2024-09-09) Udawatta P; Udayangana I; Gamage C; Shekhar R; Ranathunga S
    Code-mixing and code-switching (CMCS) are prevalent phenomena observed in social media conversations and various other modes of communication. When developing applications such as sentiment analysers and hate-speech detectors that operate on this social media data, CMCS text poses challenges. Recent studies have demonstrated that prompt-based learning of pre-trained language models outperforms full fine-tuning across various tasks. Despite the growing interest in classifying CMCS text, the effectiveness of prompt-based learning for the task remains unexplored. This paper presents an extensive exploration of prompt-based learning for CMCS text classification and the first comprehensive analysis of the impact of the script on classifying CMCS text. Our study reveals that the performance in classifying CMCS text is significantly influenced by the inclusion of multiple scripts and the intensity of code-mixing. In response, we introduce a novel method, Dynamic+AdapterPrompt, which employs distinct models for each script, integrated with adapters. While DynamicPrompt captures the script-specific representation of the text, AdapterPrompt emphasizes capturing the task-oriented functionality. Our experiments on Sinhala-English, Kannada-English, and Hindi-English datasets for sentiment classification, hate-speech detection, and humour detection tasks show that our method outperforms strong fine-tuning baselines and basic prompting strategies.
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    Word embedding evaluation for Sinhala
    (European Language Resources Association, 2020-01-01) Lakmal D; Ranathunga S; Peramuna S; Herath I; Calzolari N; Béchet F; Blache P; Choukri K; Cieri C; Declerck T; Goggi S; Isahara H; Maegaard B; Mariani J; Mazo H; Moreno A; Odijk J; Piperidis S
    This paper presents the first ever comprehensive evaluation of different types of word embeddings for Sinhala language. Three standard word embedding models, namely, Word2Vec (both Skipgram and CBOW), FastText, and Glove are evaluated under two types of evaluation methods: intrinsic evaluation and extrinsic evaluation. Word analogy and word relatedness evaluations were performed in terms of intrinsic evaluation, while sentiment analysis and part-of-speech (POS) tagging were conducted as the extrinsic evaluation tasks. Benchmark datasets used for intrinsic evaluations were carefully crafted considering specific linguistic features of Sinhala. In general, FastText word embeddings with 300 dimensions reported the finest accuracies across all the evaluation tasks, while Glove reported the lowest results.