publications

2024

1. PILAR

   2024
2. Idiomata Cognitor

   Aarón Galiano-Jiménez, Felipe Sánchez-Martínez, and Juan Antonio Pérez-Ortiz

   2024
3. Findings of the WMT 2024 Shared Task Translation into Low-Resource Languages of Spain: Blending Rule-Based and Neural Systems

   Felipe Sánchez-Martínez, Juan Antonio Perez-Ortiz, Aaron Galiano Jimenez, and 1 more author

   In Proceedings of the Ninth Conference on Machine Translation, Nov 2024

   Abs DOI

   This paper presents the results of the Ninth Conference on Machine Translation (WMT24) Shared Task “Translation into Low-Resource Languages of Spain”’. The task focused on the development of machine translation systems for three language pairs: Spanish-Aragonese, Spanish-Aranese, and Spanish-Asturian. 17 teams participated in the shared task with a total of 87 submissions. The baseline system for all language pairs was Apertium, a rule-based machine translation system that still performs competitively well, even in an era dominated by more advanced non-symbolic approaches. We report and discuss the results of the submitted systems, highlighting the strengths of both neural and rule-based approaches.

4. Hybrid Distillation from RBMT and NMT: Helsinki-NLP‘s Submission to the Shared Task on Translation into Low-Resource Languages of Spain

   Ona De Gibert, Mikko Aulamo, Yves Scherrer, and 1 more author

   In Proceedings of the Ninth Conference on Machine Translation, Nov 2024

   Abs DOI

   The Helsinki-NLP team participated in the 2024 Shared Task on Translation into Low-Resource languages of Spain with four multilingual systems covering all language pairs. The task consists in developing Machine Translation (MT) models to translate from Spanish into Aragonese, Aranese and Asturian. Our models leverage known approaches for multilingual MT, namely, data filtering, fine-tuning, data tagging, and distillation. We use distillation to merge the knowledge from neural and rule-based systems and explore the trade-offs between translation quality and computational efficiency. We demonstrate that our distilled models can achieve competitive results while significantly reducing computational costs. Our best models ranked 4th, 5th, and 2nd in the open submission track for Spanish–Aragonese, Spanish–Aranese, and Spanish–Asturian, respectively. We release our code and data publicly at https://github.com/Helsinki-NLP/lowres-spain-st.

2022

1. No Language Left Behind: Scaling Human-Centered Machine Translation

   NLLB Team, Marta R. Costa-jussà, James Cross, and 36 more authors

   Nov 2022
2. Language-agnostic BERT Sentence Embedding

   Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, and 2 more authors

   Nov 2022

2020

1. Named Entity Extraction with Finite State Transducers

   Diego Alexander Huérfano Villalba, and Elizabeth León Guzmán

   Nov 2020

   Bib

   @misc{villalba2020namedentityextractionfinite,
     title = {Named Entity Extraction with Finite State Transducers},
     author = {Villalba, Diego Alexander Huérfano and Guzmán, Elizabeth León},
     year = {2020},
     eprint = {2006.11548},
     archiveprefix = {arXiv},
     primaryclass = {cs.CL},
     url = {https://arxiv.org/abs/2006.11548}
   }

2018

1. A Neural Layered Model for Nested Named Entity Recognition

   Meizhi Ju, Makoto Miwa, and Sophia Ananiadou

   In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), Jun 2018

   Abs DOI Bib

   Entity mentions embedded in longer entity mentions are referred to as nested entities. Most named entity recognition (NER) systems deal only with the flat entities and ignore the inner nested ones, which fails to capture finer-grained semantic information in underlying texts. To address this issue, we propose a novel neural model to identify nested entities by dynamically stacking flat NER layers. Each flat NER layer is based on the state-of-the-art flat NER model that captures sequential context representation with bidirectional Long Short-Term Memory (LSTM) layer and feeds it to the cascaded CRF layer. Our model merges the output of the LSTM layer in the current flat NER layer to build new representation for detected entities and subsequently feeds them into the next flat NER layer. This allows our model to extract outer entities by taking full advantage of information encoded in their corresponding inner entities, in an inside-to-outside way. Our model dynamically stacks the flat NER layers until no outer entities are extracted. Extensive evaluation shows that our dynamic model outperforms state-of-the-art feature-based systems on nested NER, achieving 74.7% and 72.2% on GENIA and ACE2005 datasets, respectively, in terms of F-score.

   @inproceedings{ju-etal-2018-neural,
     title = {A Neural Layered Model for Nested Named Entity Recognition},
     author = {Ju, Meizhi and Miwa, Makoto and Ananiadou, Sophia},
     editor = {Walker, Marilyn and Ji, Heng and Stent, Amanda},
     booktitle = {Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
     month = jun,
     year = {2018},
     address = {New Orleans, Louisiana},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/N18-1131/},
     doi = {10.18653/v1/N18-1131},
     pages = {1446--1459}
   }

2. seqeval: A Python framework for sequence labeling evaluation

   Hiroki Nakayama

   Jun 2018

   Software available from https://github.com/chakki-works/seqeval
3. Understanding Back-Translation at Scale

   Sergey Edunov, Myle Ott, Michael Auli, and 1 more author

   Jun 2018

2016

1. OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles

   Pierre Lison, and Jörg Tiedemann

   In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC‘16), May 2016

   Abs

   We present a new major release of the OpenSubtitles collection of parallel corpora. The release is compiled from a large database of movie and TV subtitles and includes a total of 1689 bitexts spanning 2.6 billion sentences across 60 languages. The release also incorporates a number of enhancements in the preprocessing and alignment of the subtitles, such as the automatic correction of OCR errors and the use of meta-data to estimate the quality of each subtitle and score subtitle pairs.

2. Transfer Learning for Low-Resource Neural Machine Translation

   Barret Zoph, Deniz Yuret, Jonathan May, and 1 more author

   May 2016

2012

1. Parallel Data, Tools and Interfaces in OPUS

   Jörg Tiedemann

   In Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12), May 2012

2003

1. Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition

   Erik F. Tjong Kim Sang, and Fien De Meulder

   In Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, May 2003

   Bib

   @inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
     title = {Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition},
     author = {Tjong Kim Sang, Erik F. and De Meulder, Fien},
     booktitle = {Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003},
     year = {2003},
     url = {https://aclanthology.org/W03-0419/},
     pages = {142--147}
   }

2002

1. Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition

   Erik F. Tjong Kim Sang

   In COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002), May 2002

   Bib

   @inproceedings{tjong-kim-sang-2002-introduction,
     title = {Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition},
     author = {Tjong Kim Sang, Erik F.},
     booktitle = {{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)},
     year = {2002},
     url = {https://aclanthology.org/W02-2024/}
   }

2001

1. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes

   Andrew Ng, and Michael Jordan

   In Advances in Neural Information Processing Systems, May 2001

1967

1. Letters on wave mechanics

   Albert Einstein, Erwin Schrödinger, Max Planck, and 2 more authors

   May 1967

   Bib

   @book{przibram1967letters,
     title = {Letters on wave mechanics},
     author = {Einstein, Albert and Schrödinger, Erwin and Planck, Max and Lorentz, Hendrik Antoon and Przibram, Karl},
     year = {1967},
     publisher = {Vision},
   }

1956

1. Investigations on the Theory of the Brownian Movement

   Albert Einstein

   May 1956

   Bib

   @book{einstein1956investigations,
     title = {Investigations on the Theory of the Brownian Movement},
     author = {Einstein, Albert},
     year = {1956},
     publisher = {Courier Corporation},
   }

1950

1. The meaning of relativity

   Albert Einstein, and AH Taub

   American Journal of Physics, May 1950

   Bib

   @article{einstein1950meaning,
     title = {The meaning of relativity},
     author = {Einstein, Albert and Taub, AH},
     journal = {American Journal of Physics},
     volume = {18},
     number = {6},
     pages = {403--404},
     year = {1950},
     publisher = {American Association of Physics Teachers}
   }

1935

1. Can Quantum-Mechanical Description of Physical Reality Be Considered Complete?

   A. Einstein^*†, B. Podolsky^*, and N. Rosen^*

   Phys. Rev., New Jersey. More Information can be found here , May 1935

   Abs DOI HTML PDF Video

   

   In a complete theory there is an element corresponding to each element of reality. A sufficient condition for the reality of a physical quantity is the possibility of predicting it with certainty, without disturbing the system. In quantum mechanics in the case of two physical quantities described by non-commuting operators, the knowledge of one precludes the knowledge of the other. Then either (1) the description of reality given by the wave function in quantum mechanics is not complete or (2) these two quantities cannot have simultaneous reality. Consideration of the problem of making predictions concerning a system on the basis of measurements made on another system that had previously interacted with it leads to the result that if (1) is false then (2) is also false. One is thus led to conclude that the description of reality as given by a wave function is not complete.

1920

1. Relativity: the Special and General Theory

   Albert Einstein

   May 1920

   HTML

1905

1. Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen

   A. Einstein

   Annalen der physik, May 1905
2. Un the movement of small particles suspended in statiunary liquids required by the molecular-kinetic theory 0f heat

   A. Einstein

   Ann. Phys., May 1905
3. On the electrodynamics of moving bodies

   A. Einstein

   May 1905
4. Über einen die Erzeugung und Verwandlung des Lichtes betreffenden heuristischen Gesichtspunkt

   Albert Einstein

   Ann. Phys., May 1905

   Nobel Prize Abs DOI Bib

   Albert Einstein receveid the Nobel Prize in Physics 1921 for his services to Theoretical Physics, and especially for his discovery of the law of the photoelectric effect

   This is the abstract text.

   @article{einstein1905photoelectriceffect,
     title = {{{\"U}ber einen die Erzeugung und Verwandlung des Lichtes betreffenden heuristischen Gesichtspunkt}},
     author = {Einstein, Albert},
     journal = {Ann. Phys.},
     volume = {322},
     number = {6},
     pages = {132--148},
     year = {1905},
     doi = {10.1002/andp.19053220607},
   }