Papers by Francisco Guzmán
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 . |
The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English (D19-1)
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Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, Marc’Aurelio Ranzato
| Challenge: | a vast majority of language pairs in the world are considered low-resource because they have little parallel data available. |
| Approach: | They propose to use a dataset to evaluate methods trained on low-resource language pairs . they report baseline performance using supervised, weakly supervised and semi-supervised settings . |
| Outcome: | The proposed evaluation datasets show that current state-of-the-art methods perform poorly on this benchmark, posing a challenge to the research community working on low-resource MT. |
CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs (2020.emnlp-main)
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| Challenge: | Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. |
| Approach: | They exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5% across different language pairs. |
| Outcome: | The proposed method can label documents at 94.5% across languages with high precision . the proposed method is useful for low-resource languages with limited resources . |
Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications (2021.emnlp-main)
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| Challenge: | Sentence-level Quality estimation (QE) is traditionally a regression task . but large multilingual contextualized language models are expensive and infeasible for real-world applications. |
| Approach: | They evaluate several model compression techniques for QE and find they are inefficient . they argue that a full model parameterization is required to achieve SoTA results . |
| Outcome: | The proposed models are poorly expressive in a regression task, the authors argue . they show that reframing QE as a classification problem and evaluating models would improve their performance in real-world applications. |
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset (2022.lrec-1)
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Marina Fomicheva, Shuo Sun, Erick Fonseca, Chrysoula Zerva, Frédéric Blain, Vishrav Chaudhary, Francisco Guzmán, Nina Lopatina, Lucia Specia, André F. T. Martins
| Challenge: | Existing datasets for machine translation quality estimation and post-editing have several shortcomings. |
| Approach: | They propose a dataset for machine translation quality estimation and automatic post-editing . they report the performance of baseline systems trained on the MLQE-PE dataset . |
| Outcome: | The proposed dataset contains human labels for up to 10,000 translations per language pair. |
Alternative Input Signals Ease Transfer in Multilingual Machine Translation (2022.acl-long)
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| Challenge: | Recent work in multilingual machine translation (MMT) has focused on the potential of positive transfer between languages. |
| Approach: | They propose to augment training data with alternative signals that unify different writing systems, such as phonetic, romanized, and transliterated input. |
| Outcome: | The proposed model outperforms strong ensemble baselines on Indic and Turkic languages by 1.3 BLEU points on both languages. |
The Flores-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation (2022.tacl-1)
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Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzmán, Angela Fan
| Challenge: | a lack of good evaluation benchmarks hinders progress in low-resource and multilingual machine translation . despite advances in translation quality for a handful of languages, many low-source languages are not even supported by most popular translation engines. |
| Approach: | They propose a high-quality evaluation benchmark for machine translation using 3001 sentences from Wikipedia . they aim to improve evaluation of models on long tail of low-resource languages . |
| Outcome: | The proposed evaluation benchmarks are based on 3001 sentences extracted from Wikipedia . the results show that the models can be used to evaluate multilingual systems . |
Improving Zero-Shot Translation by Disentangling Positional Information (2021.acl-long)
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| Challenge: | Multilingual neural machine translation has shown the capability of directly translating between language pairs unseen in training, i.e. zero-shot translation. |
| Approach: | They propose to remove residual connections in an encoder layer to reduce the difficulty of generalizing to new translation directions. |
| Outcome: | The proposed model outperforms pivot-based translation in terms of quality and ease of integration of new languages. |
Are we Estimating or Guesstimating Translation Quality? (2020.acl-main)
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| Challenge: | A carefully engineered ensemble of pre-trained multilingual language models won the QE shared task at WMT19. |
| Approach: | They propose to use pre-trained multilingual language models to train quality estimation for machine translation. |
| Outcome: | A carefully engineered ensemble of pre-trained language models wins the QE shared task at WMT19. |
Unsupervised Cross-lingual Representation Learning at Scale (2020.acl-main)
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Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, Veselin Stoyanov
| Challenge: | Pretraining multilingual language models at scale leads to performance gains for cross-lingual transfer tasks. |
| Approach: | They present a transformer-based multilingual masked language model pre-trained on 100 languages . they show that pretraining multilingual models at scale leads to significant performance gains . |
| Outcome: | The proposed model outperforms multilingual BERT (mBERT) on cross-lingual benchmarks. |
OCR Improves Machine Translation for Low-Resource Languages (2022.findings-acl)
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| Challenge: | Despite many recent successes, Machine Translation still lacks support or fails to achieve good performance for most low-resource languages. |
| Approach: | They propose a benchmark to evaluate OCR systems on low-resource languages and low- resource scripts. |
| Outcome: | The proposed benchmark evaluates state-of-the-art OCR systems on low-resource languages and low-rural scripts. |
An Exploratory Study on Multilingual Quality Estimation (2020.aacl-main)
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Shuo Sun, Marina Fomicheva, Frédéric Blain, Vishrav Chaudhary, Ahmed El-Kishky, Adithya Renduchintala, Francisco Guzmán, Lucia Specia
| Challenge: | Existing approaches to predict the quality of machine translation use language-specific models, but they lack labelled data for each language pair. |
| Approach: | They propose to use scores from translation models to estimate quality of machine translations by predicting the quality of a translation at test time. |
| Outcome: | The proposed models outperform single-language models in less balanced quality label distributions and low-resource settings. |
Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover’s Distance (2020.aacl-main)
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| Challenge: | Document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. |
| Approach: | They propose an unsupervised scoring function that leverages cross-lingual sentence embeddings to compute the semantic distance between documents in different languages. |
| Outcome: | The proposed scoring function outperforms baseline methods on high-resource language pairs, 15% on mid-resourced language pairs and 22% on low-resourcing language pairs. |
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data (2021.acl-long)
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Wei-Jen Ko, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Naman Goyal, Francisco Guzmán, Pascale Fung, Philipp Koehn, Mona Diab
| Challenge: | linguistic overlap between low-resource languages and high-resourced languages is a major obstacle for training high-quality machine translation systems. |
| Approach: | They exploit linguistic overlap to facilitate translation to and from low-resource languages . they use monolingual data and parallel data in related high-resourced languages based on their method . |
| Outcome: | The proposed method significantly improves translation into low-resource language compared to baselines on 7 languages from three different language families. |
Putting words into the system’s mouth: A targeted attack on neural machine translation using monolingual data poisoning (2021.findings-acl)
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Jun Wang, Chang Xu, Francisco Guzmán, Ahmed El-Kishky, Yuqing Tang, Benjamin Rubinstein, Trevor Cohn
| Challenge: | Neural machine translation systems are known to be vulnerable to adversarial test inputs, however, they are also vulnerable to training attacks. |
| Approach: | They propose a poisoning attack in which a malicious adversary inserts a small poisoned sample of monolingual text into a training set of a system trained using back-translation. |
| Outcome: | The proposed attack is based on two methods that can be used to craft poisoned examples. |
Unsupervised Quality Estimation for Neural Machine Translation (2020.tacl-1)
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Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, Lucia Specia
| Challenge: | Existing approaches require large amounts of expert annotated data, computation, and time for training. |
| Approach: | They propose an unsupervised approach to QE where no training is required . they use a dataset that enables work on both black-box and glass-box approaches . |
| Outcome: | The proposed approach rivals state-of-the-art supervised QE models in terms of correlation with human judgments of quality. |
CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data (2020.lrec-1)
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Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, Edouard Grave
| Challenge: | Pre-training text representations have led to significant improvements in many areas of natural language processing. |
| Approach: | They propose a pipeline to extract monolingual datasets from Common Crawl . pipeline follows data processing introduced in fastText that deduplicates documents . |
| Outcome: | The proposed pipeline performs standard document deduplication and language identification similar to the pipeline introduced in fastText and a filtering step to select documents close to high quality corpora like Wikipedia. |
Mitigating Data Poisoning in Text Classification with Differential Privacy (2021.findings-emnlp)
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| Challenge: | Data poisoning attacks can plant a backdoor in a model by injecting poisoned examples into training data, causing the model to misclassify test instances which include a specific pattern. |
| Approach: | They propose a generic defence mechanism that makes training robust to poisoning attacks by smoothing the gradient from each training example. |
| Outcome: | The proposed method is highly effective in mitigating, or even eliminating, poisoning attacks on text classification, with only a small cost in predictive accuracy. |
Detecting Hallucinated Content in Conditional Neural Sequence Generation (2021.findings-acl)
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Chunting Zhou, Graham Neubig, Jiatao Gu, Mona Diab, Francisco Guzmán, Luke Zettlemoyer, Marjan Ghazvininejad
| Challenge: | Neural sequence models can generate fluent sentences, but they can also hallucinate additional content not supported by the input. |
| Approach: | They propose a task to predict whether each token in the output sequence is hallucinated and collect manually annotated evaluation sets for this task. |
| Outcome: | The proposed method outperforms baseline methods on machine translation and abstractive summarization datasets and achieves significant improvements in both supervised and unsupervised settings. |
Multi-Hypothesis Machine Translation Evaluation (2020.acl-main)
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| Challenge: | Reliably evaluating Machine Translation (MT) through automated metrics is a long-standing problem. |
| Approach: | They propose to use MT models to generate multiple diverse translations and use them as surrogates to reference translations to obtain a quantification of translation variability. |
| Outcome: | The proposed approach improves correlation with human judgements of quality by 15%. |
XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment (2021.emnlp-main)
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| Challenge: | Existing approaches to generate named entity lexica for lower-resource languages are under performing. |
| Approach: | They propose a technique to automatically mine cross-lingual named-entity lexica from mined web data. |
| Outcome: | The proposed technique outperforms baselines at extracting cross-lingual entity pairs and mines 164 million entity pairs from 120 different languages aligned with English. |
Quality Estimation without Human-labeled Data (2021.eacl-main)
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Yi-Lin Tuan, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Francisco Guzmán, Lucia Specia
| Challenge: | Quality estimation aims to measure the quality of translated content without access to a reference translation. |
| Approach: | They propose a method that uses synthetic training data to train supervised quality estimation models. |
| Outcome: | The proposed model outperforms models trained on human-annotated data for sentence and word-level prediction. |
As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation (2021.findings-acl)
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| Challenge: | Mistranslated numbers can cause financial loss or medical misinformation. |
| Approach: | They propose a method to assess the robustness of neural machine translation systems to numerical text via behavioural testing. |
| Outcome: | The proposed method systematically assesses four fundamental capabilities of neural machine translation systems in translation numbers by virtue of a variety of test cases. |