| Term Extraction, Tagging, and Mapping Tools for Under-Resourced Languages M Pinnis, N Ljubešić, D Ştefănescu, I Skadiņa, M Tadić, T Gornostay TKE (Terminology and Knowledge Engineering) Conference 2012, 193-208, 2012 | 69 | 2012 |
| Mitigating gender bias in machine translation with target gender annotations A Stafanovičs, T Bergmanis, M Pinnis Proceedings of the Fifth Conference on Machine Translation, 629-638, 2020 | 67 | 2020 |
| Neural machine translation for morphologically rich languages with improved sub-word units and synthetic data M Pinnis, R Krišlauks, D Deksne, T Miks Text, Speech, and Dialogue: 20th International Conference, TSD 2017, Prague …, 2017 | 49 | 2017 |
| Collecting and using comparable corpora for statistical machine translation I Skadiņa, A Aker, N Mastropavlos, F Su, D Tufis, M Verlic, A Vasiļjevs, ... Proceedings of the 8th international conference on language resources and …, 2012 | 43 | 2012 |
| Designing the Latvian Speech Recognition Corpus M Pinnis, I Auziņa, K Goba The Ninth International Conference on Language Resources and Evaluation …, 2014 | 41 | 2014 |
| Facilitating terminology translation with target lemma annotations T Bergmanis, M Pinnis arXiv preprint arXiv:2101.10035, 2021 | 40 | 2021 |
| Tilde’s Machine Translation Systems for WMT 2018 M Pinnis, M Rikters, R Krišlauks Proceedings of the Third Conference on Machine Translation: Shared Task …, 2018 | 38 | 2018 |
| Bilingual dictionaries for all EU languages A Aker, ML Paramita, M Pinnis, R Gaizauskas The Ninth International Conference on Language Resources and Evaluation …, 2014 | 31 | 2014 |
| Service model for semi-automatic generation of multilingual terminology resources A Vasiljevs, M Pinnis, T Gornostay Terminology and Knowledge Engineering 2014, 10 p, 2014 | 30 | 2014 |
| MT Adaptation for Under-Resourced Domains – What Works and What Not M Pinnis, R Skadiņš Human Language Technologies — The Baltic Perspective (HLT 2012), 176-184, 2012 | 28 | 2012 |
| Training and adapting multilingual NMT for less-resourced and morphologically rich languages M Rikters, M Pinnis, R Krišlauks Proceedings of the eleventh international conference on language resources …, 2018 | 25 | 2018 |
| Maximum entropy model for disambiguation of rich morphological tags M Pinnis, K Goba Systems and Frameworks for Computational Morphology: Second International …, 2011 | 24 | 2011 |
| Tilde’s parallel corpus filtering methods for WMT 2018 M Pinnis Proceedings of the Third Conference on Machine Translation: Shared Task …, 2018 | 23 | 2018 |
| Latvian and Lithuanian named entity recognition with TildeNER M Pinnis Seed 40, 37, 2012 | 22 | 2012 |
| Context Independent Term Mapper for European Languages M Pinnis Recent Advances in Natural Language Processing (RANLP 2013), 562--570, 2013 | 21 | 2013 |
| ACCURAT Toolkit for Multi-Level Alignment and Information Extraction from Comparable Corpora M Pinnis, R Ion, D Ştefănescu, F Su, I Skadiņa, A Vasiļjevs, B Babych ACL 2012, 4, 2012 | 19 | 2012 |
| The qt21/himl combined machine translation system JT Peter, T Alkhouli, H Ney, M Huck, F Braune, A Fraser, A Tamchyna, ... Association for Computational Linguistics, 2016 | 14 | 2016 |
| Dynamic Terminology Integration Methods in Statistical Machine Translation M Pinnis Proceedings of the Eighteenth Annual Conference of the European Association …, 2015 | 14 | 2015 |
| Pretraining and Fine-Tuning Strategies for Sentiment Analysis of Latvian Tweets G Thakkar, M Pinnis Human Language Technologies–The Baltic Perspective: Proceedings of the Ninth …, 2020 | 12 | 2020 |
| Developing and orchestrating a portfolio of natural legal language processing and document curation services G Rehm, JM Schneider, J Gracia, A Revenko, V Mireles, M Khvalchik, ... Proceedings of the Natural Legal Language Processing Workshop 2019, 55-66, 2019 | 12 | 2019 |
Andrejs VasiļjevsTilde在 tilde.com 的电子邮件经过验证
