Papers by Surangika Ranathunga
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
Shoulders of Giants: A Look at the Degree and Utility of Openness in NLP Research (2024.acl-short)
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| Challenge: | We analysed a sample of NLP research papers published in ACL Anthology . we observe a wide language-wise disparity in publicly available NLP-related artefacts . |
| Approach: | They analysed NLP research papers archived in ACL Anthology to quantify degree of openness and benefit of such an open culture in the NLP community. |
| Outcome: | The results show that more than 30% of the papers published in ACL Anthology do not release their artefacts publicly. |
BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification (2022.lrec-1)
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| Challenge: | Large-scale monolingual pre-trained language models have shown promising results for high-resource as well as lowresource languages, especially for text classification. |
| Approach: | They provide a set of recommendations for using pre-trained models for Sinhala text classification and introduce new annotated datasets useful for future research. |
| Outcome: | The proposed models are far superior to existing models for Sinhala and set a strong baseline for text classification when fine-tuned. |
Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation (2024.findings-naacl)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) methods are important in low-resource language (LRL) Neural Machine Translation (NMT) but their practical effectiveness varies significantly across different languages. |
| Approach: | They evaluated the performance of 8 parameters-efficient fine-tuning methods with 15 architectures using the SacreBLEU score. |
| Outcome: | The Houlsby+Inversion adapter outperforms the baseline architectures in both in-domain and out-domain tests and the Houlson+Inverter achieves the best performance overall. |
Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corpora (2024.eacl-long)
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| Challenge: | Existing web-mined corpora for low-resource languages have serious quality issues, especially for lowresource language pairs. |
| Approach: | They ranked each corpus according to a similarity measure and evaluated different portions of this ranked corpus. |
| Outcome: | The results show that the quality of web-mined corpora for low-resource languages is significantly different from human-curated corporats. |
Graph Based Semi-Supervised Learning Approach for Tamil POS tagging (L18-1)
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| Challenge: | Parts of Speech (POS) tagging is challenging for low resourced languages such as Tamil . low resource Tamil does not have large POS annotated corpus to build good quality POS taggers using supervised machine learning techniques. |
| Approach: | They propose a graph-based semi-supervised learning approach to classify unlabelled data using a small POS labelled data set. |
| Outcome: | The proposed method achieves 0.8743 over 0.7333 produced by a CRF tagger for the same limited size corpus. |
Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation? (2022.findings-acl)
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En-Shiun Lee, Sarubi Thillainathan, Shravan Nayak, Surangika Ranathunga, David Adelani, Ruisi Su, Arya McCarthy
| Challenge: | Pre-trained multilingual sequence-to-sequence models like mBART and mT5 can be used to translate low-resource languages, but their practical application is unclear. |
| Approach: | They conduct an empirical experiment in 10 languages to determine what can pre-trained multilingual sequence-to-sequence models like mBART do to translate low-resource languages? |
| Outcome: | The proposed models are robust to domain differences, but translations for unseen and typologically distant languages remain below 3.0 BLEU. |
Multi-lingual Mathematical Word Problem Generation using Long Short Term Memory Networks with Enhanced Input Features (2020.lrec-1)
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| Challenge: | Existing methods for multi-lingual MWP generation are incapable of identifying language specific constraints, especially in morphologically rich yet low resource languages such as Sinhala and Tamil. |
| Approach: | They propose to use a long-term memory network to generate elementary level MWPs by adding character embeddings, word embedds and Part of Speech (POS) tag embeddements to the network. |
| Outcome: | The proposed model generates elementary level MWPs while satisfying language specific constraints while providing attention for numerical values and units. |
Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World (2022.aacl-main)
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| Challenge: | Linguistic disparity in the NLP world is widely acknowledged, but the reasons behind it are rarely discussed within the field. |
| Approach: | They propose to categorise languages based on speaker population and vitality . they also analyse the distribution of language data resources and amount of NLP/CL research . |
| Outcome: | The proposed model identifies the reasons for the disparity and suggests ways to overcome it. |
Dataset and Baseline for Automatic Student Feedback Analysis (2022.lrec-1)
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| Challenge: | Currently, student feedback is collected manually, but it does not indicate the student's opinion on different aspects of the teaching/learning process. |
| Approach: | They propose to annotate student feedback corpus which contains 3000 instances . they propose a hierarchical taxonomy for aspect categorization, which covers all areas . |
| Outcome: | The proposed model can be used for aspects analysis, document level sentiment analysis and document level analysis. |
Handling Rare Word Problem using Synthetic Training Data for Sinhala and Tamil Neural Machine Translation (L18-1)
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| Challenge: | Lack of parallel training data influences rare word problem in Neural Machine Translation systems, especially for underresourced languages. |
| Approach: | They propose to use Parts of Speech tagging and morphological analysis as syntactic features to prune generated synthetic sentence pairs that do not adhere to language syntax. |
| Outcome: | The proposed methods show that they can prune sentences that do not adhere to language syntax over Sinhala to Tamil and Tamil to Sinhalak translation systems. |
Transfer Learning Based Free-Form Speech Command Classification for Low-Resource Languages (P19-2)
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| Challenge: | Current speech-based user interfaces use data intensive methodologies to recognize free-form speech commands, but this is not viable for low-resource languages, which lack speech data. |
| Approach: | They propose a method to develop a domain-specific speech command classification system using speech data from a high-resource language. |
| Outcome: | The proposed system is robust to low-resource languages with limited speech data . the proposed system achieves significant results for Sinhala and Tamil datasets . |
Word Embedding Evaluation for Sinhala (2020.lrec-1)
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| Challenge: | Word embeddings are a key component of the natural language processing process. |
| Approach: | They evaluate three standard word embedding models for Sinhala language using two evaluation methods: intrinsic evaluation and extrinsic evaluation. |
| Outcome: | The proposed models performed best in the three evaluation tasks, while FastText and Glove showed the lowest accuracies. |
Improving domain-specific SMT for low-resourced languages using data from different domains (L18-1)
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| Challenge: | Evaluation of domain-specific statistical machine translation system for official government letters . use of pseudo in-domain data showed improvement for both test sets . |
| Approach: | They develop a statistical machine translation system for official government letters . the system is based on a parallel in-domain dataset containing official letters based in Sinhala and Tamil . |
| Outcome: | The proposed system improves on the in-domain data in the domain of official government letters . the evaluations show that the system requires quality data from diverse subject matters and sources to perform better. |
Annotating Opinions and Opinion Targets in Student Course Feedback (L18-1)
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Janaka Chathuranga, Shanika Ediriweera, Ravindu Hasantha, Pranidhith Munasinghe, Surangika Ranathunga
| Challenge: | a student feedback corpus is a novel resource for opinion target extraction and sentiment analysis. |
| Approach: | They propose to annotate student feedback corpus with an opinion target extraction method and an annotation scheme for sentiment analysis. |
| Outcome: | The proposed corpus summarises student feedback on undergraduate courses . the method is difficult, and the results are presented in a tee . |
Improving the Quality of Web-mined Parallel Corpora of Low-Resource Languages using Debiasing Heuristics (2025.emnlp-main)
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| Challenge: | Parallel Data Curation (PDC) techniques aim to filter out noisy parallel sentences from web-mined corpora. |
| Approach: | They propose to rank parallel sentences using similarity scores on sentence embeddings derived from Pre-trained Multilingual Language Models (multiPLMs) . previous research has shown that the choice of multiPLM significantly impacts the quality of the filtered parallel corpus. |
| Outcome: | The proposed methods reduce disparities between multiPLMs while producing better results. |