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Matiss Rikters
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2020 – today
- 2024
- [j5]Mohammad Golam Sohrab
, Masaki Asada
BERT-NAR-BERT: A Non-Autoregressive Pre-Trained Sequence-to-Sequence Model Leveraging BERT Checkpoints. IEEE Access 12: 23-33 (2024) - [c28]Matiss Rikters, Rinalds Viksna, Edison Marrese-Taylor:
Annotations for Exploring Food Tweets from Multiple Aspects. LREC/COLING 2024: 1233-1238 - [c27]Matiss Rikters, Toshiaki Nakazawa:
Revisiting Context Choices for Context-aware Machine Translation. LREC/COLING 2024: 14073-14079 - [c26]Matiss Rikters, Makoto Miwa:
Entity-aware Multi-task Training Helps Rare Word Machine Translation. INLG 2024: 47-54 - [c25]Matiss Rikters, Makoto Miwa:
AIST AIRC Systems for the WMT 2024 Shared Tasks. WMT 2024: 286-291 - [i12]Matiss Rikters, Sanita Reinsone:
Strategic Insights in Human and Large Language Model Tactics at Word Guessing Games. CoRR abs/2409.11112 (2024) - 2023
- [c24]Matiss Rikters, Makoto Miwa:
AIST AIRC Submissions to the WMT23 Shared Task. WMT 2023: 155-161 - [i11]Maija Kale, Matiss Rikters:
What Food Do We Tweet about on a Rainy Day? CoRR abs/2304.05041 (2023) - 2022
- [j4]Matiss Rikters, Sanita Reinsone
How Masterly Are People at Playing with Their Vocabulary? Balt. J. Mod. Comput. 10(3) (2022) - [c23]Matiss Rikters, Marili Tomingas, Tuuli Tuisk, Valts Ernstreits
Machine Translation for Livonian: Catering to 20 Speakers. ACL (2) 2022: 508-514 - [i10]Matiss Rikters, Sanita Reinsone
How Masterly Are People at Playing with Their Vocabulary? Analysis of the Wordle Game for Latvian. CoRR abs/2210.01508 (2022) - 2021
- [i9]Maija Kale, Matiss Rikters:
Fragmented and Valuable: Following Sentiment Changes in Food Tweets. CoRR abs/2106.04903 (2021) - [i8]Matiss Rikters, Toshiaki Nakazawa:
Revisiting Context Choices for Context-aware Machine Translation. CoRR abs/2109.02995 (2021) - 2020
- [c22]Matiss Rikters, Toshiaki Nakazawa, Ryokan Ri:
The University of Tokyo's Submissions to the WAT 2020 Shared Task. WAT@AAC/IJCNLPL 2020: 147-153 - [c21]Rubén Martínez-Domínguez, Matiss Rikters, Arturs Vasiclevskis, Marcis Pinnis, Paula Reichenberg:
Customized Neural Machine Translation Systems for the Swiss Legal Domain. AMTA (2) 2020: 217-223 - [c20]Uga Sprogis, Matiss Rikters:
What Can We Learn from Almost a Decade of Food Tweets. Baltic HLT 2020: 191-198 - [c19]Matiss Rikters, Ryokan Ri, Tong Li, Toshiaki Nakazawa:
Document-aligned Japanese-English Conversation Parallel Corpus. WMT@EMNLP 2020: 639-645 - [i7]Uga Sprogis, Matiss Rikters:
What Can We Learn From Almost a Decade of Food Tweets. CoRR abs/2007.05194 (2020) - [i6]Matiss Rikters, Ryokan Ri, Tong Li, Toshiaki Nakazawa:
Designing the Business Conversation Corpus. CoRR abs/2008.01940 (2020) - [i5]Matiss Rikters, Ryokan Ri, Tong Li, Toshiaki Nakazawa:
Document-aligned Japanese-English Conversation Parallel Corpus. CoRR abs/2012.06143 (2020)
2010 – 2019
- 2019
- [j3]Matiss Rikters:
Hybrid Machine Translation by Combining Output from Multiple Machine Translation Systems. Balt. J. Mod. Comput. 7(3) (2019) - [c18]Matiss Rikters, Ryokan Ri, Tong Li, Toshiaki Nakazawa:
Designing the Business Conversation Corpus. WAT@EMNLP-IJCNLP 2019: 54-61 - [c17]Marcis Pinnis, Rihards Krislauks, Matiss Rikters:
Tilde's Machine Translation Systems for WMT 2019. WMT (2) 2019: 327-334 - 2018
- [j2]Matiss Rikters, Marcis Pinnis:
Debugging Translations of Transformer-based Neural Machine Translation Systems. Balt. J. Mod. Comput. 6(4) (2018) - [c16]Matiss Rikters:
Debugging Neural Machine Translations. Doctoral Consortium/Forum@DB&IS 2018: 3-12 - [c15]Toms Miks, Marcis Pinnis
Effective Online Learning Implementation for Statistical Machine Translation. DB&IS 2018: 302-313 - [c14]Matiss Rikters
Impact of Corpora Quality on Neural Machine Translation. Baltic HLT 2018: 126-133 - [c13]Matiss Rikters
Advancing Estonian Machine Translation. Baltic HLT 2018: 134-141 - [c12]Matiss Rikters, Marcis Pinnis, Rihards Krislauks:
Training and Adapting Multilingual NMT for Less-resourced and Morphologically Rich Languages. LREC 2018 - [c11]Marcis Pinnis, Matiss Rikters, Rihards Krislauks:
Tilde's Machine Translation Systems for WMT 2018. WMT (shared task) 2018: 473-481 - [i4]Matiss Rikters:
Debugging Neural Machine Translations. CoRR abs/1808.02733 (2018) - [i3]Matiss Rikters:
Impact of Corpora Quality on Neural Machine Translation. CoRR abs/1810.08392 (2018) - 2017
- [j1]Matiss Rikters, Mark Fishel, Ondrej Bojar:
Visualizing Neural Machine Translation Attention and Confidence. Prague Bull. Math. Linguistics 109: 39-50 (2017) - [c10]Matiss Rikters, Ondrej Bojar:
Paying Attention to Multi-Word Expressions in Neural Machine Translation. MTSummit (1) 2017: 86-95 - [c9]Matiss Rikters, Mark Fishel:
Confidence through Attention. MTSummit (1) 2017: 299-311 - [c8]Matiss Rikters, Chantal Amrhein, Maksym Del, Mark Fishel:
C-3MA: Tartu-Riga-Zurich Translation Systems for WMT17. WMT 2017: 382-388 - [i2]Matiss Rikters, Mark Fishel:
Confidence through Attention. CoRR abs/1710.03743 (2017) - [i1]Matiss Rikters, Ondrej Bojar:
Paying Attention to Multi-Word Expressions in Neural Machine Translation. CoRR abs/1710.06313 (2017) - 2016
- [c7]Matiss Rikters
Interactive Multi-System Machine Translation with Neural Language Models. DB&IS (Selected Papers) 2016: 269-280 - [c6]Matiss Rikters
K-Translate - Interactive Multi-system Machine Translation. DB&IS 2016: 304-318 - [c5]Matiss Rikters
Combining Machine Translated Sentence Chunks from Multiple MT Systems. CICLing (2) 2016: 27-37 - [c4]Matiss Rikters
Searching for the Best Translation Combination Across All Possible Variants. Baltic HLT 2016: 92-96 - [c3]Matiss Rikters:
Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation. HyTra@COLING 2016: 8-15 - [c2]Matiss Rikters, Inguna Skadina:
Syntax-based Multi-system Machine Translation. LREC 2016 - 2015
- [c1]Matiss Rikters:
Multi-system machine translation using online APIs for English-Latvian. HyTra@ACL 2015: 6-10
Coauthor Index
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