Research work

I’m broadly interested in building equitable language technologies in the presence of severe constraints— such as the lack of data or compute. My current research interests are low-resource and multilingual NLP, efficient NLP, and corpus linguistics.

You can also check my Google Scholar and Semantic Scholar profiles for more info.

2024

• RewardBench: Evaluating Reward Models
  Nathan Lambert, Valentina Pyatkin, Jacob Morrison, LJ Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, and Hannaneh Hajishirzi. Preprint. arXiv:2403.13787 [cs.LG]. March 2024.
  [Leaderboard] [Code] [Blog]

• Allen Institute for AI @ SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages. Lester James V. Miranda, Proceedings of the EACL 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP. ACL. St. Julian’s, Malta. March 2024.
  [Code] [Video]

2023

• calamanCy: a Tagalog Natural Language Processing Toolkit
  Lester James V. Miranda, Proceedings of the EMNLP 2023 Workshop on NLP Open Source Software (NLP-OSS). EMNLP. Singapore, Singapore. December 2023.
  [Code] [Poster] [Video]

• Developing a Named Entity Recognition Dataset for Tagalog
  Lester James V. Miranda, Proceedings of the IJCNLP-AACL 2023 Workshop on Southeast Asian Language Processing (SEALP). ACL. Nusa Dua, Bali, Indonesia. November 2023.
  [Code] [Dataset] [Video]

• Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark
  Stephen Mayhew, Terra Blevins, Shuheng Liu, Marek Šuppa, Hila Gonen, Joseph Marvin Imperial, Börje F. Karlsson, Peiqin Lin, Nikola Ljubešić, LJ Miranda, Barbara Plank, Arij Riabi, Yuval Pinter. Preprint. arXiv:2311.09122 [cs.CL]. November 2023.
  [Dataset] [Website]

2022

• Multi hash embeddings in spaCy
  Lester James V. Miranda*, Ákos Kádár*, Adriane Boyd, Sofie Van Landeghem, Anders Søgaard, and Matthew Honnibal. Preprint. arXiv:2212.09255 [cs.CL]. November 2022.
  (∗: equal contributions)
  [Code]

 

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Previous research

I used to be a bioinformatics researcher at the Furuzuki Neurocomputing Systems Laboratory, working on nature-inspired algorithms and proteomics.

• Feature Extraction using a Mutually-Competitive Autoencoder for Protein Function Prediction. Lester James V. Miranda and Jinglu Hu, IEEE International Conference on System, Man, and Cybernetics (SMC). IEEE. Miyazaki, Japan. October 2018.

• A Deep Learning Approach based on Stacked Denoising Autoencoders for Protein Function Prediction. Lester James V. Miranda and Jinglu Hu, 42nd IEEE Computer Society Signature Conference on Computers, Software, and Applications (COMPSAC). IEEE. Tokyo, Japan. July 2018.

• PySwarms, a research-toolkit for Particle Swarm Optimization in Python
  Lester James V. Miranda, Journal of Open Source Software, vol. 3, no. 433, 2018.

I was also involved in research early on during my undergrad:

• Appliance Recognition using Hall-Effect Sensors and k-Nearest Neighbors for Power Management Systems. Lester James V. Miranda*, Marian Joice Gutierrez*, Samuel Matthew Dumlao, and Rosula Reyes, Proceedings of the 2016 IEEE Region 10 Conference 2016 (TENCON). IEEE. Singapore. November 2016. (∗: equal contributions)