Eric Joanis


2020

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The Indigenous Languages Technology project at NRC Canada: An empowerment-oriented approach to developing language software
Roland Kuhn | Fineen Davis | Alain Désilets | Eric Joanis | Anna Kazantseva | Rebecca Knowles | Patrick Littell | Delaney Lothian | Aidan Pine | Caroline Running Wolf | Eddie Santos | Darlene Stewart | Gilles Boulianne | Vishwa Gupta | Brian Maracle Owennatékha | Akwiratékha’ Martin | Christopher Cox | Marie-Odile Junker | Olivia Sammons | Delasie Torkornoo | Nathan Thanyehténhas Brinklow | Sara Child | Benoît Farley | David Huggins-Daines | Daisy Rosenblum | Heather Souter
Proceedings of the 28th International Conference on Computational Linguistics

This paper surveys the first, three-year phase of a project at the National Research Council of Canada that is developing software to assist Indigenous communities in Canada in preserving their languages and extending their use. The project aimed to work within the empowerment paradigm, where collaboration with communities and fulfillment of their goals is central. Since many of the technologies we developed were in response to community needs, the project ended up as a collection of diverse subprojects, including the creation of a sophisticated framework for building verb conjugators for highly inflectional polysynthetic languages (such as Kanyen’kéha, in the Iroquoian language family), release of what is probably the largest available corpus of sentences in a polysynthetic language (Inuktut) aligned with English sentences and experiments with machine translation (MT) systems trained on this corpus, free online services based on automatic speech recognition (ASR) for easing the transcription bottleneck for recordings of speech in Indigenous languages (and other languages), software for implementing text prediction and read-along audiobooks for Indigenous languages, and several other subprojects.

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The Nunavut Hansard Inuktitut–English Parallel Corpus 3.0 with Preliminary Machine Translation Results
Eric Joanis | Rebecca Knowles | Roland Kuhn | Samuel Larkin | Patrick Littell | Chi-kiu Lo | Darlene Stewart | Jeffrey Micher
Proceedings of the 12th Language Resources and Evaluation Conference

The Inuktitut language, a member of the Inuit-Yupik-Unangan language family, is spoken across Arctic Canada and noted for its morphological complexity. It is an official language of two territories, Nunavut and the Northwest Territories, and has recognition in additional regions. This paper describes a newly released sentence-aligned Inuktitut–English corpus based on the proceedings of the Legislative Assembly of Nunavut, covering sessions from April 1999 to June 2017. With approximately 1.3 million aligned sentence pairs, this is, to our knowledge, the largest parallel corpus of a polysynthetic language or an Indigenous language of the Americas released to date. The paper describes the alignment methodology used, the evaluation of the alignments, and preliminary experiments on statistical and neural machine translation (SMT and NMT) between Inuktitut and English, in both directions.

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Findings of the 2020 Conference on Machine Translation (WMT20)
Loïc Barrault | Magdalena Biesialska | Ondřej Bojar | Marta R. Costa-jussà | Christian Federmann | Yvette Graham | Roman Grundkiewicz | Barry Haddow | Matthias Huck | Eric Joanis | Tom Kocmi | Philipp Koehn | Chi-kiu Lo | Nikola Ljubešić | Christof Monz | Makoto Morishita | Masaaki Nagata | Toshiaki Nakazawa | Santanu Pal | Matt Post | Marcos Zampieri
Proceedings of the Fifth Conference on Machine Translation

This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.

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Improving Parallel Data Identification using Iteratively Refined Sentence Alignments and Bilingual Mappings of Pre-trained Language Models
Chi-kiu Lo | Eric Joanis
Proceedings of the Fifth Conference on Machine Translation

The National Research Council of Canada’s team submissions to the parallel corpus filtering task at the Fifth Conference on Machine Translation are based on two key components: (1) iteratively refined statistical sentence alignments for extracting sentence pairs from document pairs and (2) a crosslingual semantic textual similarity metric based on a pretrained multilingual language model, XLM-RoBERTa, with bilingual mappings learnt from a minimal amount of clean parallel data for scoring the parallelism of the extracted sentence pairs. The translation quality of the neural machine translation systems trained and fine-tuned on the parallel data extracted by our submissions improved significantly when compared to the organizers’ LASER-based baseline, a sentence-embedding method that worked well last year. For re-aligning the sentences in the document pairs (component 1), our statistical approach has outperformed the current state-of-the-art neural approach in this low-resource context.

2014

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Coarse “split and lump” bilingual language models for richer source information in SMT
Darlene Stewart | Roland Kuhn | Eric Joanis | George Foster
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

Recently, there has been interest in automatically generated word classes for improving statistical machine translation (SMT) quality: e.g, (Wuebker et al, 2013). We create new models by replacing words with word classes in features applied during decoding; we call these “coarse models”. We find that coarse versions of the bilingual language models (biLMs) of (Niehues et al, 2011) yield larger BLEU gains than the original biLMs. BiLMs provide phrase-based systems with rich contextual information from the source sentence; because they have a large number of types, they suffer from data sparsity. Niehues et al (2011) mitigated this problem by replacing source or target words with parts of speech (POSs). We vary their approach in two ways: by clustering words on the source or target side over a range of granularities (word clustering), and by clustering the bilingual units that make up biLMs (bitoken clustering). We find that loglinear combinations of the resulting coarse biLMs with each other and with coarse LMs (LMs based on word classes) yield even higher scores than single coarse models. When we add an appealing “generic” coarse configuration chosen on English > French devtest data to four language pairs (keeping the structure fixed, but providing language-pair-specific models for each pair), BLEU gains on blind test data against strong baselines averaged over 5 runs are +0.80 for English > French, +0.35 for French > English, +1.0 for Arabic > English, and +0.6 for Chinese > English.

2013

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Transferring markup tags in statistical machine translation: a two-stream approach
Eric Joanis | Darlene Stewart | Samuel Larkin | Roland Kuhn
Proceedings of the 2nd Workshop on Post-editing Technology and Practice

2010

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Lessons from NRC’s Portage System at WMT 2010
Samuel Larkin | Boxing Chen | George Foster | Ulrich Germann | Eric Joanis | Howard Johnson | Roland Kuhn
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

2009

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PortageLive: delivering machine translation technology via virtualization
Patrick Paul | Samuel Larkin | Ulrich Germann | Eric Joanis | Roland Kuhn
Proceedings of Machine Translation Summit XII: Plenaries

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Tightly Packed Tries: How to Fit Large Models into Memory, and Make them Load Fast, Too
Ulrich Germann | Eric Joanis | Samuel Larkin
Proceedings of the Workshop on Software Engineering, Testing, and Quality Assurance for Natural Language Processing (SETQA-NLP 2009)

2007

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Integration of an Arabic Transliteration Module into a Statistical Machine Translation System
Mehdi M. Kashani | Eric Joanis | Roland Kuhn | George Foster | Fred Popowich
Proceedings of the Second Workshop on Statistical Machine Translation

2006

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PORTAGE: with Smoothed Phrase Tables and Segment Choice Models
Howard Johnson | Fatiha Sadat | George Foster | Roland Kuhn | Michel Simard | Eric Joanis | Samuel Larkin
Proceedings on the Workshop on Statistical Machine Translation

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Segment Choice Models: Feature-Rich Models for Global Distortion in Statistical Machine Translation
Roland Kuhn | Denis Yuen | Michel Simard | Patrick Paul | George Foster | Eric Joanis | Howard Johnson
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

2003

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Semi-supervised Verb Class Discovery Using Noisy Features
Suzanne Stevenson | Eric Joanis
Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003

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A General Feature Space for Automatic Verb Classification
Eric Joanis | Suzanne Stevenson
10th Conference of the European Chapter of the Association for Computational Linguistics