Papers by Holger Schwenk
Filtering and Mining Parallel Data in a Joint Multilingual Space (P18-2)
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| Challenge: | Using a cosine distance in a joint multilingual sentence embedding, we filter out noisy parallel data and mine for bitexts in large news collections. |
| Approach: | They propose to learn a joint multilingual sentence embedding and use the distance between sentences in different languages to filter noisy parallel data and to mine for parallel data in large monolingual texts. |
| Outcome: | The proposed approach improves a competitive baseline on the WMT'14 task by 0.3 BLEU by filtering out 25% of the training data. |
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia (2021.eacl-main)
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| Challenge: | a new approach to extract parallel sentences from Wikipedia articles is proposed . the approach is based on multilingual sentence embeddings, but does not limit it to English . |
| Approach: | They propose to automatically extract parallel sentences from Wikipedia articles in 96 languages . they train neural MT baseline systems on the mined data and evaluate them on the TED corpus . |
| Outcome: | The proposed approach extracts parallel sentences from Wikipedia articles in 96 languages . the extracted sentences achieve strong BLEU scores for many language pairs . |
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings (P19-1)
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| Challenge: | Traditional parallel corpus mining methods focus on the textual content instead of the size and quality of training data. |
| Approach: | They propose a method for machine translation based on multilingual sentence embeddings. |
| Outcome: | The proposed method outperforms the best published methods on the BUCC mining task and the UN reconstruction task by more than 10 F1 and 30 precision points. |
SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations (2023.acl-long)
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Paul-Ambroise Duquenne, Hongyu Gong, Ning Dong, Jingfei Du, Ann Lee, Vedanuj Goswami, Changhan Wang, Juan Pino, Benoît Sagot, Holger Schwenk
| Challenge: | SpeechMatrix is a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings. |
| Approach: | They present a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings. |
| Outcome: | The proposed model can train bilingual models on 136 language pairs with 418 thousand hours of speech. |
CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web (2021.acl-long)
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| Challenge: | Using a curated common crawl corpus, we were able to mine 10.8 billion parallel sentences out of which only 2.9 billions are aligned with English. |
| Approach: | They use 32 snapshots of a curated common crawl corpus totaling 71 billion unique sentences to mine 10.8 billion parallel sentences out of which only 2.9 billions are aligned with English. |
| Outcome: | The proposed system outperforms the best single systems on the WMT’19 test set for English-German/Russian/Chinese and outperformed the best submission at the 2020 WAT workshop. |
MLQA: Evaluating Cross-lingual Extractive Question Answering (2020.acl-main)
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| Challenge: | Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. |
| Approach: | They present a multi-way aligned extractive QA evaluation benchmark in 7 languages . they evaluate state-of-the-art cross-lingual models and machine-translation-based baselines . |
| Outcome: | The proposed model is based on MLQA, which has over 12K instances in english and 5K in each other language. |
A Corpus for Multilingual Document Classification in Eight Languages (L18-1)
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| Challenge: | a subset of the Reuters corpus volume 2 is used to evaluate cross-lingual document classification . current best practice is to evaluate document classification on resources in one language and transfer it to another without additional resources. |
| Approach: | They propose to use a subset of the Reuters corpus to evaluate cross-lingual document classification . they propose to add Italian, Russian, Japanese and Chinese to the subset . |
| Outcome: | The proposed subset of the Reuters corpus has balanced class priors for eight languages. |
Speech-to-Speech Translation for a Real-world Unwritten Language (2023.findings-acl)
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Peng-Jen Chen, Kevin Tran, Yilin Yang, Jingfei Du, Justine Kao, Yu-An Chung, Paden Tomasello, Paul-Ambroise Duquenne, Holger Schwenk, Hongyu Gong, Hirofumi Inaguma, Sravya Popuri, Changhan Wang, Juan Pino, Wei-Ning Hsu, Ann Lee
| Challenge: | a new study examines speech-to-speech translation (S2ST) that translates speech from one language into another . the research area for unwritten languages remains a research area with little exploration due to the lack of training data. |
| Approach: | They propose a system that translates speech from one language into another . they use Taiwanese Hokkien as an example of an unwritten language . |
| Outcome: | The proposed system can be used to train models in languages without standard writing systems. |
Textless Speech-to-Speech Translation on Real Data (2022.naacl-main)
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Ann Lee, Hongyu Gong, Paul-Ambroise Duquenne, Holger Schwenk, Peng-Jen Chen, Changhan Wang, Sravya Popuri, Yossi Adi, Juan Pino, Jiatao Gu, Wei-Ning Hsu
| Challenge: | Existing text-based speech-to-speech translation systems rely on cascaded approach . text-to text translation systems require text generation and a single input to generate output . |
| Approach: | They propose a textless speech-to-speech translation system that can translate speech from one language into another without the need of text data. |
| Outcome: | The proposed system can translate speech from one language into another without text data. |
Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages (2022.findings-emnlp)
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| Challenge: | a new study aims to extend multilingual representation learning beyond the hundred most frequent languages . current work on multilingual sentence representations has focused on training one model which handles all languages of interest . |
| Approach: | They propose a teacher-student approach to extend existing monolingual sentence embedding space to new languages. |
| Outcome: | The proposed model outperforms the original LASER encoder in 44 African languages . the model can be used to train multiple languages and learn new languages if they have the same training data . |
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. |
xSIM++: An Improved Proxy to Bitext Mining Performance for Low-Resource Languages (2023.acl-short)
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| Challenge: | xsim++ provides a reliable proxy for bitext mining without expensive pipelines. |
| Approach: | They propose a new proxy proxy based on similarity in a multilingual embedding space . they validate this proxy by running a significant number of bitext mining experiments for a set of low-resource languages and then train NMT systems on the mined data. |
| Outcome: | The proposed proxy improves on xsim++ and trains on the mined data. |
LCFO: Long Context and Long Form Output Dataset and Benchmarking (2025.findings-acl)
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Marta R. Costa-jussà, Pierre Andrews, Mariano Coria Meglioli, Joy Chen, Joe Chuang, David Dale, Christophe Ropers, Alexandre Mourachko, Eduardo Sánchez, Holger Schwenk, Tuan A. Tran, Arina Turkatenko, Carleigh Wood
| Challenge: | Using long text outputs to evaluate progress in summarization and summary expansion tasks is challenging. |
| Approach: | They propose a framework for assessing gradual summarization and summary expansion capabilities across diverse domains. |
| Outcome: | The proposed framework provides alignments between specific QA pairs and corresponding summaries in 7 domains. |
XNLI: Evaluating Cross-lingual Sentence Representations (D18-1)
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Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, Veselin Stoyanov
| Challenge: | State-of-the-art natural language processing systems rely on annotated data to learn competent models. |
| Approach: | They extend the development and test sets of the Multi-Genre Natural Language Inference Corpus to 14 languages, including Swahili and Urdu. |
| Outcome: | The proposed evaluation set extends the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 14 languages including low-resource languages such as Swahili and Urdu. |
Multilingual Representation Distillation with Contrastive Learning (2023.eacl-main)
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| Challenge: | Contextual representations from large pretrained language models encode semantic information from two or more languages. |
| Approach: | They integrate contrastive learning into multilingual representation distillation and use it for quality estimation of parallel sentences. |
| Outcome: | The proposed model outperforms existing models with similarity searches and filtering tasks across low-resource languages. |
stopes - Modular Machine Translation Pipelines (2022.emnlp-demos)
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Pierre Andrews, Guillaume Wenzek, Kevin Heffernan, Onur Çelebi, Anna Sun, Ammar Kamran, Yingzhe Guo, Alexandre Mourachko, Holger Schwenk, Angela Fan
| Challenge: | Neural machine translation is a natural language deep learning application that needs data to be trained. |
| Approach: | They describe a framework that empowers scalability and versatility for research use cases. |
| Outcome: | The proposed framework empowers scalability and versatility for research use cases. |
BLASER: A Text-Free Speech-to-Speech Translation Evaluation Metric (2023.acl-long)
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Mingda Chen, Paul-Ambroise Duquenne, Pierre Andrews, Justine Kao, Alexandre Mourachko, Holger Schwenk, Marta R. Costa-jussà
| Challenge: | End-to-End speech-to speech translation is generally evaluated with text-based metrics . this means generated speech has to be automatically transcribed, making the evaluation dependent on ASR systems. |
| Approach: | They propose a text-free evaluation metric for end-to-end speech-tospeech translation, named BLASER, to avoid the dependency on automatic speech recognition systems. |
| Outcome: | The proposed metric avoids the dependency on automatic speech recognition systems by encoding generated speech segments into a shared embedding space. |
T-Modules: Translation Modules for Zero-Shot Cross-Modal Machine Translation (2022.emnlp-main)
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| Challenge: | Existing approaches to perform zero-shot cross-modal transfer between speech and text are limited to a very small number of language pairs. |
| Approach: | They propose a method to perform zero-shot cross-modal transfer between speech and text for translation tasks by using a speech decoder. |
| Outcome: | The proposed model significantly improves state-of-the-art for zero-shot speech translation on Must-C. |