publications

2026

1. Confident but Wrong: A Constrained Decoding Diagnostic for Low-Resource Automatic Post-Editing

   Isuru Wijesiri and others

   In Findings of the Association for Computational Linguistics: EMNLP 2026, 2026

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   A black-box, inference-time diagnostic for automatic post-editing (APE) that varies an edit-distance penalty between free editing and copying the machine-translation output. The resulting TER-vs-penalty curve exposes two systematic failure modes of low-resource fine-tuning, Binary Collapse and Confident Miscalibration, and prescribes a fix. The work releases the first English-Sinhala ( 66k) and a new English-Tamil ( 39k) APE datasets.

2. EnSiTa: A Trilingual Multi-Domain Parallel Dataset and Benchmark for Domain-Specific Machine Translation

   Isuru Wijesiri and others

   Preprint (under submission, targeting TACL), 2026

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   An English-Sinhala-Tamil parallel dataset built by professional post-editing of machine translation and web-mined corpora across multiple domains, released with a benchmark for domain-specific machine translation. It is the source of the APE data used in the EMNLP 2026 paper.

2020

1. Memory-Efficient Graph Convolutional Network-Based Distributed Link Prediction

   Isuru Wijesiri and others

   In 2020 IEEE International Conference on Big Data (Big Data), 2020

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   Introduces a memory-efficient, federated training scheme that partitions large graphs across JasmineGraph and runs parallel GCN workers. This lets a single commodity server train link-prediction models on datasets as big as DBLP-V11 (10+ GB) that conventional GCNs cannot load, and cuts training time by 3-5x on Reddit and Twitter graphs compared with standard, in-memory methods.

2. Anomaly Detection using Deep Reconstruction and Forecasting for Autonomous Systems

   Isuru Wijesiri and others

   arXiv preprint arXiv:2006.14556, 2020

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   Self-supervised anomaly-detection system for autonomous drones: forecasted next video frame with a conditional GAN and IMU vectors with dual LSTM (autoencoder + forecaster); achieved 94% accuracy / 0.95 F1 on video, 100% accuracy / 0.98 F1 on IMU, earning Runner-up, IEEE Signal Processing Cup 2020.