LoResMT 2023

Workshop on Low-Resource Machine Translation


  • Dubrovnik, Croatia


Important Dates

Submission deadline 02 March
Notification of acceptance 23 March
Camera-ready papers deadline 01 April
LoResMT workshop 02 May


Day 1

9:00 Opening remarks
Workshop Chairs
9:15 Invited talk
Crawling your way out of less-resourcedness
Nikola Ljubešić
Chair: Atul Kr. Ojha
10:05 Session 1: Finding Papers
Chair: Sina Ahmadi
10:05 Machine Translation between Spoken Languages and Signed Languages Represented in SignWriting
Zifan Jiang, Amit Moryossef, Mathias Müller, Sarah Ebling
10:20 Decipherment as Regression: Solving Historical Substitution Ciphers by Learning Symbol Recurrence Relations
Nishant Kambhatla, Logan Born, Anoop Sarkar
10:35 ☕️
11:15 Session 2: Scientific Research Papers
Chair: Ekaterina Vylomova
11:15 Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages
Zhong Zhou, Jan Niehues, Alexander Waibel
11:35 Measuring the Impact of Data Augmentation Methods for Extremely Low-Resource NMT
Annie Lamar, Zeyneb N. Kaya
11:55 Language-Family Adapters for Low-Resource Multilingual Neural Machine Translation
Alexandra Chronopoulou, Dario Stojanovski, Alexander Fraser
12:15 Multilingual Bidirectional Unsupervised Translation through Multilingual Finetuning and Back-Translation
Bryan Li, Mohammad Sadegh Rasooli, Ajay Patel, Chris Callison-Burch
12:45 🍴
14:15 Applying Lessons from Low-Resource Machine Translation to Speech and Sign Language Translation
Rico Sennrich
Chair: Chao-Hong Liu
15:00 Session 3: Finding Papers
Chair: Nathaniel Oco
15:00 Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings?
Sonal Sannigrahi, Josef van Genabith, Cristina España-Bonet
15:15 Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages
Ankan Mullick, Ishani Mondal, Sourjyadip Ray, Raghav R, G Chaitanya, Pawan Goyal
15:30 A Simplified Training Pipeline for Low-Resource and Unsupervised Machine Translation
Àlex R. Atrio, Alexis Allemann, Ljiljana Dolamic, Andrei Popescu-Belis
15:45 ☕️
16:30 Session 4: Scientific Research Papers
Chair: Valentin Malykh
16:30 Improving Neural Machine Translation of Indigenous Languages with Multilingual Transfer Learning
Wei-Rui Chen, Muhammad Abdul-Mageed
16:50 PEACH: Pre-Training Sequence-to-Sequence Multilingual Models for Translation with Semi-Supervised Pseudo-Parallel Document Generation
Alireza Salemi, Amirhossein Abaskohi, Sara Tavakoli, Azadeh Shakery, Yadollah Yaghoobzadeh
17:10 Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentation
Injy Hamed, Nizar Habash, Slim Abdennadher, Ngoc Thang Vu
17:30 Evaluating Sentence Alignment Methods in a Low-Resource Setting: An English-YorùBá Study Case
Edoardo Signoroni, Pavel Rychlý
17:50 Findings from the Bambara - French Machine Translation Competition (BFMT 2023)
Ninoh Agostinho Da Silva, Tunde Ajayi, Alex Antonov, Panga Azazia Kamate, Moussa Coulibaly, Mason Del Rio, Yacouba Diarra, Sebastian Diarra, Chris Emezue, Joel Hamilcaro, Christopher Homan, Alexander Most, Joseph Mwatukange, Peter Ohue, Michael Pham, Abdoulaye Sako, Sokhar Samb, Yaya Sy, Tharindu Cyril Weerasooriya, Yacine Zahidi, Sarah Luger
18:05 Closing remarks
Workshop Chairs


  • 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 MT
  • 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 machine translation, 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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