LoResMT 2022

Workshop on Low-Resource Machine Translation

The fifth Low Resource Machine Translation (LoResMT 2022) workshop was held by the 29th International Conference on Computational Linguistics (COLING) on 16 October, 2022, in Gyeongju, Republic of Korea.


09:00 - 09:15 Opening remarks
Workshop chairs
09:15 - 10:05 Invited talk
Mining Methods for Low Resource MT
Vishrav Chaudhary, Microsoft Turing
Chair: Atul Kr. Ojha
10:05 - 10:30 Q&A Session 1
Chair: Ekaterina Vylomova
  Very Low Resource Sentence Alignment: Luhya and Swahili
Everlyn Chimoto, Bruce Bassett
  A Preordered RNN Layer Boosts Neural Machine Translation in Low Resource Settings
Mohaddeseh Bastan, Shahram Khadivi
10:30 - 11:00 ☕️
11:00 - 12:30 Q&A Session 2
Chair: Jonathan Washington
  Known Words Will Do: Unknown Concept Translation via Lexical Relations
Winston Wu, David Yarowsky
  The only chance to understand: machine translation of the severely endangered low-resource languages of Eurasia
Anna Mosolova, Kamel Smaili
  Data-adaptive Transfer Learning for Translation: A Case Study in Haitian and Jamaican
Nathaniel Robinson, Cameron Hogan, Nancy Fulda, David R. Mortensen
12:30 - 14:00 🍴
14:00 - 14:55 Invited talk
Low Resource Machine Translation- A Perspective
Pushpak Bhattacharyya, Indian Institute of Technology Bombay
Chair: Chao-Hong Liu
14:55 - 15:30 Q&A Session 3
Chair: Nathaniel Oco
  Augmented Bio-SBERT: Improving Performance For Pairwise Sentence Tasks in Bio-medical Domain
Sonam Pankaj, Amit Gautam
  Machine Translation for a very Low-Resource Language - Layer Freezing approach on Transfer Learning
Amartya Chowdhury, Deepak K. T., Samudra Vijaya K, S. R. Mahadeva Prasanna
  HFT: High Frequency Tokens for Low-Resource NMT
Edoardo Signoroni, Pavel Rychlý
15:30 - 16:00 ☕️
16:00 - 17:00 Q&A Session 4
Chair: Valentin Malykh
  Romanian language translation in the RELATE platform
Vasile Pais, Maria Mitrofan, Andrei-Marius Avram
  Translating Spanish into Spanish Sign Language: Combining Rules and Data-driven Approaches
Luis Chiruzzo, Euan McGill, Santiago Egea-Gómez, Horacio Saggion
17:00 - 17:50 Q&A Session 5
Chair: Xiaobing Zhao
  Benefiting from Language Similarity in the Multilingual MT Training: Case Study of Indonesian and Malaysian
Alberto Poncelas, Johanes Effendi
  Multiple Pivot Languages and Strategic Decoder Initialization helps Neural Machine Translation
Shivam Mhaskar, Pushpak Bhattacharyya
  Exploring Word Alignment Towards an Efficient Sentence Aligner for Filipino and Cebuano Languages
Jenn Leana Fernandez, Kristine Mae M. Adlaon
  Aligning Word Vectors on Low-Resource Languages with Wiktionary
Mike Lzbicki
17:50 - 18:00 Closing remarks
Workshop chairs

Important dates

Submission deadline 30 July
Notification of acceptance 22 August
Camera-ready papers deadline 5 September
LoResMT workshop 16 October

All deadlines are anywhere on Earth.


  • COVID-related corpora, their translations and corresponding natural language processing/machine translation systems
  • Neural machine translation for low-resource languages
  • Work that presents online systems for practical use by native speakers
  • Word tokenisers/de-tokenisers for specific languages
  • Word/morpheme segmenters for specific languages
  • Alignment/Re-ordering tools for specific language pairs
  • Use of morphology analysers and/or morpheme segmenters in machine translation
  • Multilingual/cross-lingual natural language processing tools for machine translation
  • Corpora creation and curation technologies for low-resource languages
  • Review of available parallel corpora for low-resource languages
  • Research and review papers of machine translation methods for low-resource languages
  • Machine translation systems/methods (for example, rule-based, statistical, neural machine translation) for low-resource languages
  • Pivot machine translation for low-resource languages
  • Zero-shot machine translation for low-resource languages
  • Fast building of machine translation systems for low-resource languages
  • Re-usability of existing machine translation systems for low-resource languages
  • Machine translation for language preservation

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