Pierre Andrews


2023

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Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scale
Marta Costa-jussà | Pierre Andrews | Eric Smith | Prangthip Hansanti | Christophe Ropers | Elahe Kalbassi | Cynthia Gao | Daniel Licht | Carleigh Wood
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

We introduce a multilingual extension of the HolisticBias dataset, the largest English template-based taxonomy of textual people references: Multilingual HolisticBias. This extension consists of 20,459 sentences in 50 languages distributed across 13 demographic axes. Source sentences are built from combinations of 118 demographic descriptors and three patterns, excluding nonsensical combinations. Multilingual translations include alternatives for gendered languages that cover gendered translations when there is ambiguity in English. Our dataset is intended to uncover demographic imbalances and be the tool to quantify mitigations towards them. Our initial findings show that translation quality for EN-to-XX translations is an average of almost 8 spBLEU better when evaluating with the masculine human reference compared to feminine. In the opposite direction, XX-to-EN, we compare the robustness of the model when the source input only differs in gender (masculine or feminine) and masculine translations are an average of almost 4 spBLEU better than feminine. When embedding sentences to a joint multilingual sentence representations space, we find that for most languages masculine translations are significantly closer to the English neutral sentences when embedded.

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The Gender-GAP Pipeline: A Gender-Aware Polyglot Pipeline for Gender Characterisation in 55 Languages
Benjamin Muller | Belen Alastruey | Prangthip Hansanti | Elahe Kalbassi | Christophe Ropers | Eric Smith | Adina Williams | Luke Zettlemoyer | Pierre Andrews | Marta R. Costa-jussà
Proceedings of the Eighth Conference on Machine Translation

Gender biases in language generation systems are challenging to mitigate. One possible source for these biases is gender representation disparities in the training and evaluation data. Despite recent progress in documenting this problem and many attempts at mitigating it, we still lack shared methodology and tooling to report gender representation in large datasets. Such quantitative reporting will enable further mitigation, e.g., via data augmentation. This paper describes the Gender-Gap Pipeline (for Gender-Aware Polyglot Pipeline), an automatic pipeline to characterize gender representation in large-scale datasets for 55 languages. The pipeline uses a multilingual lexicon of gendered person-nouns to quantify the gender representation in text. We showcase it to report gender representation in WMT training data and development data for the News task, confirming that current data is skewed towards masculine representation. Having unbalanced datasets may indirectly optimize our systems towards outperforming one gender over the others. We suggest introducing our gender quantification pipeline in current datasets and, ideally, modifying them toward a balanced representation.

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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.

2022

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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.

2009

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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]

2008

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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