Pierre Andrews


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BLASER: A Text-Free Speech-to-Speech Translation Evaluation Metric
Mingda Chen | Paul-Ambroise Duquenne | Pierre Andrews | Justine Kao | Alexandre Mourachko | Holger Schwenk | Marta R. Costa-jussà
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

End-to-End speech-to-speech translation (S2ST) is generally evaluated with text-based metrics. This means that generated speech has to be automatically transcribed, making the evaluation dependent on the availability and quality of automatic speech recognition (ASR) systems. In this paper, we propose a text-free evaluation metric for end-to-end S2ST, named BLASER, to avoid the dependency on ASR systems. BLASER leverages a multilingual multimodal encoder to directly encode the speech segments for source input, translation output and reference into a shared embedding space and computes a score of the translation quality that can be used as a proxy to human evaluation. To evaluate our approach, we construct training and evaluation sets from more than 40k human annotations covering seven language directions. The best results of BLASER are achieved by training with supervision from human rating scores. We show that when evaluated at the sentence level, BLASER correlates significantly better with human judgment compared to ASR dependent metrics including ASR-SENTBLEU in all translation directions and ASR-COMET in five of them. Our analysis shows combining speech and text as inputs to BLASER does not increase the correlation with human scores, but best correlations are achieved when using speech, which motivates the goal of our research. Moreover, we show that using ASR for references is detrimental for text-based metrics.


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stopes - Modular Machine Translation Pipelines
Pierre Andrews | Guillaume Wenzek | Kevin Heffernan | Onur Çelebi | Anna Sun | Ammar Kamran | Yingzhe Guo | Alexandre Mourachko | Holger Schwenk | Angela Fan
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Neural machine translation, as other natural language deep learning applications, is hungry for data. As research evolves, the data pipelines supporting that research evolve too, oftentimes re-implementing the same core components. Despite the potential of modular codebases, researchers have but little time to put code structure and reusability first. Unfortunately, this makes it very hard to publish clean, reproducible code to benefit a wider audience. In this paper, we motivate and describe stopes , a framework that addresses these issues while empowering scalability and versatility for research use cases. This library was a key enabler of the No Language Left Behind project, establishing new state of the art performance for a multilingual machine translation model covering 200 languages. stopes and the pipelines described are released under the MIT license at https://github.com/facebookresearch/stopes.


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A SVM Cascade for Agreement/Disagreement Classification
Pierre Andrews | Suresh Manandhar
Traitement Automatique des Langues, Volume 50, Numéro 3 : Apprentissage automatique pour le TAL [Machine Learning for NLP]


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Argumentative Human Computer Dialogue for Automated Persuasion
Pierre Andrews | Suresh Manandhar | Marco De Boni
Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue