Cyril Goutte

Also published as: C. Goutte


2021

pdf bib
N-gram and Neural Models for Uralic Language Identification: NRC at VarDial 2021
Gabriel Bernier-Colborne | Serge Leger | Cyril Goutte
Proceedings of the Eighth Workshop on NLP for Similar Languages, Varieties and Dialects

We describe the systems developed by the National Research Council Canada for the Uralic language identification shared task at the 2021 VarDial evaluation campaign. We evaluated two different approaches to this task: a probabilistic classifier exploiting only character 5-grams as features, and a character-based neural network pre-trained through self-supervision, then fine-tuned on the language identification task. The former method turned out to perform better, which casts doubt on the usefulness of deep learning methods for language identification, where they have yet to convincingly and consistently outperform simpler and less costly classification algorithms exploiting n-gram features.

2020

pdf bib
Human or Neural Translation?
Shivendra Bhardwaj | David Alfonso Hermelo | Phillippe Langlais | Gabriel Bernier-Colborne | Cyril Goutte | Michel Simard
Proceedings of the 28th International Conference on Computational Linguistics

Deep neural models tremendously improved machine translation. In this context, we investigate whether distinguishing machine from human translations is still feasible. We trained and applied 18 classifiers under two settings: a monolingual task, in which the classifier only looks at the translation; and a bilingual task, in which the source text is also taken into consideration. We report on extensive experiments involving 4 neural MT systems (Google Translate, DeepL, as well as two systems we trained) and varying the domain of texts. We show that the bilingual task is the easiest one and that transfer-based deep-learning classifiers perform best, with mean accuracies around 85% in-domain and 75% out-of-domain .

pdf bib
Challenges in Neural Language Identification: NRC at VarDial 2020
Gabriel Bernier-Colborne | Cyril Goutte
Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects

We describe the systems developed by the National Research Council Canada for the Uralic language identification shared task at the 2020 VarDial evaluation campaign. Although our official results were well below the baseline, we show in this paper that this was not due to the neural approach to language identification in general, but to a flaw in the function we used to sample data for training and evaluation purposes. Preliminary experiments conducted after the evaluation period suggest that our neural approach to language identification can achieve state-of-the-art results on this task, although further experimentation is required.

2019

pdf bib
Improving Cuneiform Language Identification with BERT
Gabriel Bernier-Colborne | Cyril Goutte | Serge Léger
Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects

We describe the systems developed by the National Research Council Canada for the Cuneiform Language Identification (CLI) shared task at the 2019 VarDial evaluation campaign. We compare a state-of-the-art baseline relying on character n-grams and a traditional statistical classifier, a voting ensemble of classifiers, and a deep learning approach using a Transformer network. We describe how these systems were trained, and analyze the impact of some preprocessing and model estimation decisions. The deep neural network achieved 77% accuracy on the test data, which turned out to be the best performance at the CLI evaluation, establishing a new state-of-the-art for cuneiform language identification.

2018

pdf bib
Real-time Change Point Detection using On-line Topic Models
Yunli Wang | Cyril Goutte
Proceedings of the 27th International Conference on Computational Linguistics

Detecting changes within an unfolding event in real time from news articles or social media enables to react promptly to serious issues in public safety, public health or natural disasters. In this study, we use on-line Latent Dirichlet Allocation (LDA) to model shifts in topics, and apply on-line change point detection (CPD) algorithms to detect when significant changes happen. We describe an on-line Bayesian change point detection algorithm that we use to detect topic changes from on-line LDA output. Extensive experiments on social media data and news articles show the benefits of on-line LDA versus standard LDA, and of on-line change point detection compared to off-line algorithms. This yields F-scores up to 52% on the detection of significant real-life changes from these document streams.

pdf bib
EuroGames16: Evaluating Change Detection in Online Conversation
Cyril Goutte | Yunli Wang | Fangming Liao | Zachary Zanussi | Samuel Larkin | Yuri Grinberg
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
Measuring sentence parallelism using Mahalanobis distances: The NRC unsupervised submissions to the WMT18 Parallel Corpus Filtering shared task
Patrick Littell | Samuel Larkin | Darlene Stewart | Michel Simard | Cyril Goutte | Chi-kiu Lo
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

The WMT18 shared task on parallel corpus filtering (Koehn et al., 2018b) challenged teams to score sentence pairs from a large high-recall, low-precision web-scraped parallel corpus (Koehn et al., 2018a). Participants could use existing sample corpora (e.g. past WMT data) as a supervisory signal to learn what a “clean” corpus looks like. However, in lower-resource situations it often happens that the target corpus of the language is the only sample of parallel text in that language. We therefore made several unsupervised entries, setting ourselves an additional constraint that we not utilize the additional clean parallel corpora. One such entry fairly consistently scored in the top ten systems in the 100M-word conditions, and for one task—translating the European Medicines Agency corpus (Tiedemann, 2009)—scored among the best systems even in the 10M-word conditions.

pdf bib
Accurate semantic textual similarity for cleaning noisy parallel corpora using semantic machine translation evaluation metric: The NRC supervised submissions to the Parallel Corpus Filtering task
Chi-kiu Lo | Michel Simard | Darlene Stewart | Samuel Larkin | Cyril Goutte | Patrick Littell
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We present our semantic textual similarity approach in filtering a noisy web crawled parallel corpus using YiSi—a novel semantic machine translation evaluation metric. The systems mainly based on this supervised approach perform well in the WMT18 Parallel Corpus Filtering shared task (4th place in 100-million-word evaluation, 8th place in 10-million-word evaluation, and 6th place overall, out of 48 submissions). In fact, our best performing system—NRC-yisi-bicov is one of the only four submissions ranked top 10 in both evaluations. Our submitted systems also include some initial filtering steps for scaling down the size of the test corpus and a final redundancy removal step for better semantic and token coverage of the filtered corpus. In this paper, we also describe our unsuccessful attempt in automatically synthesizing a noisy parallel development corpus for tuning the weights to combine different parallelism and fluency features.

2017

pdf bib
Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags
Yunli Wang | Cyril Goutte
Proceedings of the Events and Stories in the News Workshop

Detecting events from social media data has important applications in public security, political issues, and public health. Many studies have focused on detecting specific or unspecific events from Twitter streams. However, not much attention has been paid to detecting changes, and their impact, in online conversations related to an event. We propose methods for detecting such changes, using clustering of temporal profiles of hashtags, and three change point detection algorithms. The methods were tested on two Twitter datasets: one covering the 2014 Ottawa shooting event, and one covering the Sochi winter Olympics. We compare our approach to a baseline consisting of detecting change from raw counts in the conversation. We show that our method produces large gains in change detection accuracy on both datasets.

pdf bib
Exploring Optimal Voting in Native Language Identification
Cyril Goutte | Serge Léger
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

We describe the submissions entered by the National Research Council Canada in the NLI-2017 evaluation. We mainly explored the use of voting, and various ways to optimize the choice and number of voting systems. We also explored the use of features that rely on no linguistic preprocessing. Long ngrams of characters obtained from raw text turned out to yield the best performance on all textual input (written essays and speech transcripts). Voting ensembles turned out to produce small performance gains, with little difference between the various optimization strategies we tried. Our top systems achieved accuracies of 87% on the essay track, 84% on the speech track, and close to 92% by combining essays, speech and i-vectors in the fusion track.

2016

pdf bib
Discriminating Similar Languages: Evaluations and Explorations
Cyril Goutte | Serge Léger | Shervin Malmasi | Marcos Zampieri
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present an analysis of the performance of machine learning classifiers on discriminating between similar languages and language varieties. We carried out a number of experiments using the results of the two editions of the Discriminating between Similar Languages (DSL) shared task. We investigate the progress made between the two tasks, estimate an upper bound on possible performance using ensemble and oracle combination, and provide learning curves to help us understand which languages are more challenging. A number of difficult sentences are identified and investigated further with human annotation

pdf bib
Advances in Ngram-based Discrimination of Similar Languages
Cyril Goutte | Serge Léger
Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)

We describe the systems entered by the National Research Council in the 2016 shared task on discriminating similar languages. Like previous years, we relied on character ngram features, and a mixture of discriminative and generative statistical classifiers. We mostly investigated the influence of the amount of data on the performance, in the open task, and compared the two-stage approach (predicting language/group, then variant) to a flat approach. Results suggest that ngrams are still state-of-the-art for language and variant identification, and that additional data has a small but decisive impact.

pdf bib
Extracting Discriminative Keyphrases with Learned Semantic Hierarchies
Yunli Wang | Yong Jin | Xiaodan Zhu | Cyril Goutte
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

The goal of keyphrase extraction is to automatically identify the most salient phrases from documents. The technique has a wide range of applications such as rendering a quick glimpse of a document, or extracting key content for further use. While previous work often assumes keyphrases are a static property of a given documents, in many applications, the appropriate set of keyphrases that should be extracted depends on the set of documents that are being considered together. In particular, good keyphrases should not only accurately describe the content of a document, but also reveal what discriminates it from the other documents. In this paper, we study this problem of extracting discriminative keyphrases. In particularly, we propose to use the hierarchical semantic structure between candidate keyphrases to promote keyphrases that have the right level of specificity to clearly distinguish the target document from others. We show that such knowledge can be used to construct better discriminative keyphrase extraction systems that do not assume a static, fixed set of keyphrases for a document. We show how this helps identify key expertise of authors from their papers, as well as competencies covered by online courses within different domains.

pdf bib
CNRC at SemEval-2016 Task 1: Experiments in Crosslingual Semantic Textual Similarity
Chi-kiu Lo | Cyril Goutte | Michel Simard
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

pdf bib
Towards Automatic Description of Knowledge Components
Cyril Goutte | Guillaume Durand | Serge Léger
Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications

pdf bib
Experiments in Discriminating Similar Languages
Cyril Goutte | Serge Léger
Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects

2014

pdf bib
Linear Mixture Models for Robust Machine Translation
Marine Carpuat | Cyril Goutte | George Foster
Proceedings of the Ninth Workshop on Statistical Machine Translation

pdf bib
The NRC System for Discriminating Similar Languages
Cyril Goutte | Serge Léger | Marine Carpuat
Proceedings of the First Workshop on Applying NLP Tools to Similar Languages, Varieties and Dialects

pdf bib
CNRC-TMT: Second Language Writing Assistant System Description
Cyril Goutte | Michel Simard | Marine Carpuat
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

2013

pdf bib
Feature Space Selection and Combination for Native Language Identification
Cyril Goutte | Serge Léger | Marine Carpuat
Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications

2012

pdf bib
Learning Machine Translation from In-domain and Out-of-domain Data
Marco Turchi | Cyril Goutte | Nello Cristianini
Proceedings of the 16th Annual conference of the European Association for Machine Translation

pdf bib
The Impact of Sentence Alignment Errors on Phrase-Based Machine Translation Performance
Cyril Goutte | Marine Carpuat | George Foster
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers

When parallel or comparable corpora are harvested from the web, there is typically a tradeoff between the size and quality of the data. In order to improve quality, corpus collection efforts often attempt to fix or remove misaligned sentence pairs. But, at the same time, Statistical Machine Translation (SMT) systems are widely assumed to be relatively robust to sentence alignment errors. However, there is little empirical evidence to support and characterize this robustness. This contribution investigates the impact of sentence alignment errors on a typical phrase-based SMT system. We confirm that SMT systems are highly tolerant to noise, and that performance only degrades seriously at very high noise levels. Our findings suggest that when collecting larger, noisy parallel data for training phrase-based SMT, cleaning up by trying to detect and remove incorrect alignments can actually degrade performance. Although fixing errors, when applicable, is a preferable strategy to removal, its benefits only become apparent for fairly high misalignment rates. We provide several explanations to support these findings.

2010

pdf bib
Discriminative Instance Weighting for Domain Adaptation in Statistical Machine Translation
George Foster | Cyril Goutte | Roland Kuhn
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

pdf bib
Automatic Detection of Translated Text and its Impact on Machine Translation
David Kurokawa | Cyril Goutte | Pierre Isabelle
Proceedings of Machine Translation Summit XII: Papers

pdf bib
Improving SMT by learning translation direction
Cyril Goutte | David Kurokawa | Pierre Isabelle
Proceedings of the 13th Annual conference of the European Association for Machine Translation

2007

pdf bib
Statistical Phrase-Based Post-Editing
Michel Simard | Cyril Goutte | Pierre Isabelle
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

pdf bib
Domain adaptation of MT systems through automatic post-editing
Pierre Isabelle | Cyril Goutte | Michel Simard
Proceedings of Machine Translation Summit XI: Papers

2005

pdf bib
Translating with Non-contiguous Phrases
Michel Simard | Nicola Cancedda | Bruno Cavestro | Marc Dymetman | Eric Gaussier | Cyril Goutte | Kenji Yamada | Philippe Langlais | Arne Mauser
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

pdf bib
Une approche à la traduction automatique statistique par segments discontinus
Michel Simard | Nicola Cancedda | Bruno Cavestro | Marc Dymetman | Eric Gaussier | Cyril Goutte | Philippe Langlais | Arne Mauser | Kenji Yamada
Actes de la 12ème conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

Cet article présente une méthode de traduction automatique statistique basée sur des segments non-continus, c’est-à-dire des segments formés de mots qui ne se présentent pas nécéssairement de façon contiguë dans le texte. On propose une méthode pour produire de tels segments à partir de corpus alignés au niveau des mots. On présente également un modèle de traduction statistique capable de tenir compte de tels segments, de même qu’une méthode d’apprentissage des paramètres du modèle visant à maximiser l’exactitude des traductions produites, telle que mesurée avec la métrique NIST. Les traductions optimales sont produites par le biais d’une recherche en faisceau. On présente finalement des résultats expérimentaux, qui démontrent comment la méthode proposée permet une meilleure généralisation à partir des données d’entraînement.

2004

pdf bib
Aligning words using matrix factorisation
Cyril Goutte | Kenji Yamada | Eric Gaussier
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

pdf bib
A Geometric View on Bilingual Lexicon Extraction from Comparable Corpora
Eric Gaussier | J.M. Renders | I. Matveeva | C. Goutte | H. Dejean
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

pdf bib
Confidence Estimation for Machine Translation
John Blatz | Erin Fitzgerald | George Foster | Simona Gandrabur | Cyril Goutte | Alex Kulesza | Alberto Sanchis | Nicola Ueffing
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2003

pdf bib
Reducing Parameter Space for Word Alignment
Herve Dejean | Eric Gaussier | Cyril Goutte | Kenji Yamada
Proceedings of the HLT-NAACL 2003 Workshop on Building and Using Parallel Texts: Data Driven Machine Translation and Beyond

2002

pdf bib
Combining Labelled and Unlabelled Data: A Case Study on Fisher Kernels and Transductive Inference for Biological Entity Recognition
Cyril Goutte | Hervé Déjean | Eric Gaussier | Nicola Cancedda | Jean-Michel Renders
COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002)