Papers by Loic Barrault
Evaluation of Lifelong Learning Systems (2020.lrec-1)
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| Challenge: | Current intelligent systems need the expensive support of machine learning experts to sustain their performance level when used on a daily basis. |
| Approach: | They propose a generic evaluation methodology for lifelong learning systems . they use "initialisation data" to refer to the set of training, development and test data together . |
| Outcome: | The proposed evaluation method is based on the evaluation of human-assisted learning outside the context of lifelong learning. |
Aligning Speech Segments Beyond Pure Semantics (2024.findings-acl)
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| Challenge: | Existing speech-to-speech parallel data is scarce and expensive to create from scratch. |
| Approach: | They propose an algorithm which automatically aligns pairs of speech segments aligned in meaning and expressivity. |
| Outcome: | The proposed algorithm outperforms semantic-focused approaches on content translation quality. |
Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better (2023.acl-long)
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| Challenge: | a recent study shows that without artificially encouraging models to hallucinate, existing methods fall short . hallucinations are cases when the model generates output that is partially or fully unrelated to the source sentence. |
| Approach: | They propose a method that evaluates the percentage of the source contribution to a generated translation. |
| Outcome: | The proposed method improves detection accuracy for the most severe hallucinations by a factor of 2. |
MEXMA: Token-level objectives improve sentence representations (2025.acl-long)
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| Challenge: | Current approaches to cross-lingual sentence encoders use sentence-level objectives only. |
| Approach: | They propose a novel approach that integrates both sentence-level and token-level objectives. |
| Outcome: | The proposed approach outperforms existing CLSEs on bitext mining tasks and downstream tasks. |
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning (2022.acl-short)
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| Challenge: | Recent active learning approaches in NLP use off-the-shelf pretrained language models (LMs) . a poor training strategy can be catastrophic for AL, authors argue . |
| Approach: | They propose to first adapt the pretrained LM to the target task and then use it for AL. |
| Outcome: | The proposed approach provides substantial data efficiency improvements compared to the standard fine-tuning approach. |
Mixture of Languages: Improved Multilingual Encoders Through Language Grouping (2025.emnlp-main)
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João Maria Janeiro, Belen Alastruey, Francisco Massa, Maha Elbayad, Benjamin Piwowarski, Patrick Gallinari, Loic Barrault
| Challenge: | Recent work in this field relies on training transformer encoders on a large amount of multilingual data, with all parameters shared across all languages. |
| Approach: | They propose a mixture of languages strategy to pretrain largely multilingual encoders using masked language modeling. |
| Outcome: | The proposed architecture outperforms a dense counterpart, MoE models and public multilingual encoders on downstream tasks while minimizing interference. |
Metaphor Detection with Effective Context Denoising (2023.eacl-main)
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| Challenge: | Existing models focus on semantically relevant information and provide a target-oriented parse tree structure for metaphor detection. |
| Approach: | They propose a new model which introduces a target-oriented parse tree structure for metaphor detection. |
| Outcome: | The proposed model achieves state-of-the-art on several main metaphor datasets and compares with other methods. |
FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning (2023.eacl-main)
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| Challenge: | Existing models for concept-level metaphor detection lack explicit knowledge of FrameNet . Metaphor detection is a pervasive linguistic device that is used in cognitive and communicative functions of language. |
| Approach: | They propose a BERT-based model that explicitly learns FrameNet Embeddings for metaphor detection. |
| Outcome: | The proposed model is more explainable and interpretable than existing models. |
HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translation (2023.emnlp-main)
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David Dale, Elena Voita, Janice Lam, Prangthip Hansanti, Christophe Ropers, Elahe Kalbassi, Cynthia Gao, Loic Barrault, Marta Costa-jussà
| Challenge: | Previously available quality assessments do not distinguish between hallucinations and omissions. |
| Approach: | They propose to annotate hallucinations and omissions in machine translation using a single language pair. |
| Outcome: | The proposed dataset covers 18 translation directions with varying resource levels and scripts. |