Papers by Laurent Besacier
Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models (2022.acl-long)
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| Challenge: | Multi-encoder models aim to improve translation quality by encoding document-level contextual information alongside the current sentence. |
| Approach: | They propose to pre-train contextual parameters over split sentence pairs to improve contextual encoding . they propose four different splitting methods to improve learning of contextual parameters . |
| Outcome: | The proposed model improves learning of contextual parameters, both in low and high resource settings. |
Fashioning Local Designs from Generic Speech Technologies in an Australian Aboriginal Community (2022.coling-1)
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| Challenge: | Recent research has focused on low-resource languages and the transcription bottleneck paradigm. |
| Approach: | They propose to use a spoken term detection system to train a speech recognition system in an Aboriginal community to reach better comprehension and engagement from Aboriginal participants. |
| Outcome: | The proposed system can be implemented in an Aboriginal community and reach better comprehension and engagement from Aboriginal participants. |
FlauBERT: Unsupervised Language Model Pre-training for French (2020.lrec-1)
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Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoit Crabbé, Laurent Besacier, Didier Schwab
| Challenge: | Language models are a key step to achieve state-of-the-art results in many different Natural Language Processing (NLP) tasks. |
| Approach: | They propose to use a language model that is pre-trained on a large and heterogeneous French corpus to train continuous word representations. |
| Outcome: | The proposed model outperforms existing models on a large and heterogeneous French corpus. |
Weakly Supervised Word Segmentation for Computational Language Documentation (2022.acl-long)
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| Challenge: | a recent paper aims to improve the effectiveness of unsupervised language analysis techniques in low resource settings. |
| Approach: | They propose to use a weak supervision to improve linguistic segmentation in low resource languages . they propose to provide linguists with LTs that can be used to create interactive annotation tools . |
| Outcome: | The proposed models can be used to improve the quality of language segmentation in low resource languages. |
Naver Labs Europe’s Systems for the Document-Level Generation and Translation Task at WNGT 2019 (D19-56)
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| Challenge: | Recent advances in machine translation and natural language generation have created many challenges in this field especially when context is considered. |
| Approach: | They propose to leverage data from machine translation and natural language generation tasks to do transfer learning between MT, NLG and MT with source-side metadata. |
| Outcome: | The proposed approach outperforms the previous state-of-the-art on the Rotowire NLG task. |
Do Multilingual Neural Machine Translation Models Contain Language Pair Specific Attention Heads? (2021.findings-acl)
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| Challenge: | Recent studies on multilingual representations focus on whether there is an emergence of language-independent representations or whether multilingual models partition their weights among different languages. |
| Approach: | They analyze encoder self-attention and encoder-decoder attention heads in a multilingual neural translation model. |
| Outcome: | The proposed model is based on a multilingual neural translation model with a language-independent representation. |
Monolingual Adapters for Zero-Shot Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing adapter layers are more parameter-efficient and provide better performance than bilingual ones. |
| Approach: | They propose to use monolingual adapter layers instead of bilingual ones to compose them and generalize to unseen language pairs. |
| Outcome: | The proposed adapter layer formalism achieves a median improvement of +2.77 BLEU points over a 20-language multilingual Transformer baseline trained on TED talks. |
Multilingual Unsupervised Neural Machine Translation with Denoising Adapters (2021.emnlp-main)
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| Challenge: | Multilingual unsupervised machine translation is a computationally expensive and hard to tune approach . auxiliary parallel data is used to train translation systems from monolingual data . |
| Approach: | They propose to use auxiliary parallel language pairs to train unsupervised machine translations . they propose to add auxiliary languages to pre-trained mBART-50 models with denoising adapters . |
| Outcome: | The proposed approach is on-par with back-translation and allows adding unseen languages incrementally. |
Gender Representation in Open Source Speech Resources (2020.lrec-1)
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| Challenge: | Using open source corpora, we find that gender balance depends on other corpus characteristics such as elicited/non ellicite vs. non-eliciting speech, low/high resource language, speech task targeted. |
| Approach: | They propose to use open source corpora to find gender information in spoken language systems . they propose metadata and recommendations for researchers to assure better transparency . |
| Outcome: | The proposed method improves the quality and transparency of open source speech resources. |
What Do Compressed Multilingual Machine Translation Models Forget? (2022.findings-emnlp)
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Alireza Mohammadshahi, Vassilina Nikoulina, Alexandre Berard, Caroline Brun, James Henderson, Laurent Besacier
| Challenge: | Recent studies show that pre-trained models achieve state-of-the-art results in NLP tasks but their size makes it more challenging to apply them in resource-constrained environments. |
| Approach: | They assess the impact of compression methods on multilingual Neural Machine Translation models for various language groups, gender, and semantic biases. |
| Outcome: | The proposed compression methods improve models on different benchmarks for language groups, gender, and semantic biases. |
SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages (2022.emnlp-main)
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Alireza Mohammadshahi, Vassilina Nikoulina, Alexandre Berard, Caroline Brun, James Henderson, Laurent Besacier
| Challenge: | Existing models for multilingual machine translation use scaling up the number of parameters to overcome the curse of multilinguality. |
| Approach: | They propose a multilingual machine translation model that shares information between similar languages and scales up the number of parameters to overcome the curse of multilinguality. |
| Outcome: | The proposed model outperforms previous models on low-resource benchmarks while improving inference latency and memory usage. |
Learning From Failure: Data Capture in an Australian Aboriginal Community (2022.acl-long)
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| Challenge: | a prototype of a language data capture app for speakers was tested in an Aboriginal community . elicitation of word lists, phrases, etc. has been used for decades to collect data for Indigenous languages . many software tools are developed to support linguists' work . |
| Approach: | They propose to deploy an app for speakers to confirm system guesses in an approach to transcription based on word spotting. |
| Outcome: | The proposed app was tested in an Aboriginal community in australia . it was able to confirm system guesses without a transcription bottleneck . the results were compared with other apps in the community . |
A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments (L18-1)
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Pierre Godard, Gilles Adda, Martine Adda-Decker, Juan Benjumea, Laurent Besacier, Jamison Cooper-Leavitt, Guy-Noel Kouarata, Lori Lamel, Hélène Maynard, Markus Mueller, Annie Rialland, Sebastian Stueker, François Yvon, Marcely Zanon-Boito
| Challenge: | a new study aims to document endangered languages using a speech corpus . linguistic documentation is limited to the phonetic, lexical and syntactic levels . |
| Approach: | They propose to use a speech corpus to document endangered languages in field . they propose to collect 5k speech utterances aligned to French text translations . |
| Outcome: | The proposed language corpus is used to document endangered languages in field linguists . it is multilingual and contains 5k speech utterances aligned to french text translations - the authors show it can be used in a zero-resource task . |
Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English (2020.coling-main)
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Maha Elbayad, Michael Ustaszewski, Emmanuelle Esperança-Rodier, Francis Brunet-Manquat, Jakob Verbeek, Laurent Besacier
| Challenge: | Existing studies compare offline and online neural machine translation architectures . we examine the impact of online decoding constraints on the translation quality . |
| Approach: | They evaluate offline and online neural machine translation architectures using human evaluations on English-German and German-English language pairs. |
| Outcome: | The proposed models are particularly sensitive to latency constraints and are well-suited for offline translation tasks. |
Enabling Interactive Transcription in an Indigenous Community (2020.coling-main)
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| Challenge: | Existing methods for manual transcription are often in isolation from the speech community, and so we miss out on the opportunity to take advantage of the interests and skills of local people. |
| Approach: | They propose a transcription workflow which combines spoken term detection and human-in-the-loop to support speech transcription in almost-zero resource settings. |
| Outcome: | The proposed workflow is based on two endangered languages with zero-resource datasets. |
Lightweight Adapter Tuning for Multilingual Speech Translation (2021.acl-short)
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| Challenge: | Adapter tuning is an efficient alternative to fine-tuning in NLP . a multilingual model could be outperformed by its bilingual counterparts . |
| Approach: | They propose to use adapter tuning to optimize for multilingual speech translation . they use pre-trained models to freeze pre-train parameters and inject lightweight modules . |
| Outcome: | The proposed adapters can specialize to specific language pairs with low extra cost . the proposed models outperform bilingual models on high-resource language pairs . |
ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language Models (2025.coling-main)
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| Challenge: | Existing benchmarks for long-context LLMs focus on generic tasks that are not necessarily aligned with real-world applications. |
| Approach: | They propose to augment existing ELITR corpus by adding 271 manually crafted questions with their ground-truth answers and noisy versions of meeting transcripts altered to target different Word Error Rate levels. |
| Outcome: | The proposed benchmark augments the existing ELITR corpus by adding 271 manually crafted questions with ground-truth answers, as well as noisy versions of meeting transcripts altered to target different Word Error Rate levels. |
Speech Foundation Models and Crowdsourcing for Efficient, High-Quality Data Collection (2025.coling-main)
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| Challenge: | Existing methods for crowdsourcing data collection require a human workforce, which is hard to sustain. |
| Approach: | They propose to use Speech Foundation Models to automate validation processes . they find that SFMs can reduce reliance on human validation . |
| Outcome: | The proposed model reduces the reliance on human validation without degrading the quality of the final data. |
Token-level and sequence-level loss smoothing for RNN language models (P18-1)
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| Challenge: | Maximum likelihood estimation treats all sentences that do not match the ground truth as equally poor, ignoring the structure of the output space. |
| Approach: | They propose to extend the reward augmented maximum likelihood approach to token-level loss smoothing by using token-based approaches to improve the model's performance. |
| Outcome: | The proposed model improves on image captioning and machine translation tasks and treats all sentences that do not match the ground truth as poor . |
Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation (L18-1)
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| Challenge: | Recent work in spoken language translation (SLT) has attempted to build end-to-end speech-totext translation without using source language transcription during learning or decoding. |
| Approach: | They propose to augment an existing (monolingual) corpus: LibriSpeech. |
| Outcome: | The proposed corpus is derived from read audiobooks from the LibriVox project and has been carefully segmented and aligned. |
MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible (2020.lrec-1)
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| Challenge: | The Bible is the same for all the languages, thus constituting a multilingual and comparable 2 spoken corpus, is not exploited to date. |
| Approach: | They propose to add multilingual links between small speech segments in different languages . they use a large dataset of 8,130 parallel spoken utterances across 8 languages - maSS . |
| Outcome: | The proposed model can build automatic speech recognition models for 700 languages. |
Parallel Corpora in Mboshi (Bantu C25, Congo-Brazzaville) (L18-1)
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Annie Rialland, Martine Adda-Decker, Guy-Noël Kouarata, Gilles Adda, Laurent Besacier, Lori Lamel, Elodie Gauthier, Pierre Godard, Jamison Cooper-Leavitt
| Challenge: | BULB project aims to provide tools to language documentation and description for unwritten languages . language-based technologies are needed to support the collection of data and to provide linguistic documentation for the languages. |
| Approach: | This paper presents multimodal and parallel data collections in Mboshi, as part of the French-German BULB project. |
| Outcome: | The proposed data collection includes pictures and videos documenting social practices, agriculture, wildlife and plants. |
Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech Translation (2020.coling-main)
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| Challenge: | Existing models for automatic speech recognition and multilingual speech translation are on par with cascade counterparts. |
| Approach: | They propose a dual-decoder Transformer architecture that performs automatic speech recognition and multilingual speech translation. |
| Outcome: | The proposed models outperform the previously-reported highest translation performance in multilingual settings and bilingual one-to-one results. |