Ahmed El-Kishky


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Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data
Wei-Jen Ko | Ahmed El-Kishky | Adithya Renduchintala | Vishrav Chaudhary | Naman Goyal | Francisco Guzmán | Pascale Fung | Philipp Koehn | Mona Diab
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages. Fortunately, some low-resource languages are linguistically related or similar to high-resource languages; these related languages may share many lexical or syntactic structures. In this work, we exploit this linguistic overlap to facilitate translating to and from a low-resource language with only monolingual data, in addition to any parallel data in the related high-resource language. Our method, NMT-Adapt, combines denoising autoencoding, back-translation and adversarial objectives to utilize monolingual data for low-resource adaptation. We experiment on 7 languages from three different language families and show that our technique significantly improves translation into low-resource language compared to other translation baselines.

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Putting words into the system’s mouth: A targeted attack on neural machine translation using monolingual data poisoning
Jun Wang | Chang Xu | Francisco Guzmán | Ahmed El-Kishky | Yuqing Tang | Benjamin Rubinstein | Trevor Cohn
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation
Jun Wang | Chang Xu | Francisco Guzmán | Ahmed El-Kishky | Benjamin Rubinstein | Trevor Cohn
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications
Shuo Sun | Ahmed El-Kishky | Vishrav Chaudhary | James Cross | Lucia Specia | Francisco Guzmán
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Sentence-level Quality estimation (QE) of machine translation is traditionally formulated as a regression task, and the performance of QE models is typically measured by Pearson correlation with human labels. Recent QE models have achieved previously-unseen levels of correlation with human judgments, but they rely on large multilingual contextualized language models that are computationally expensive and make them infeasible for real-world applications. In this work, we evaluate several model compression techniques for QE and find that, despite their popularity in other NLP tasks, they lead to poor performance in this regression setting. We observe that a full model parameterization is required to achieve SoTA results in a regression task. However, we argue that the level of expressiveness of a model in a continuous range is unnecessary given the downstream applications of QE, and show that reframing QE as a classification problem and evaluating QE models using classification metrics would better reflect their actual performance in real-world applications.

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XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment
Ahmed El-Kishky | Adithya Renduchintala | James Cross | Francisco Guzmán | Philipp Koehn
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Cross-lingual named-entity lexica are an important resource to multilingual NLP tasks such as machine translation and cross-lingual wikification. While knowledge bases contain a large number of entities in high-resource languages such as English and French, corresponding entities for lower-resource languages are often missing. To address this, we propose Lexical-Semantic-Phonetic Align (LSP-Align), a technique to automatically mine cross-lingual entity lexica from mined web data. We demonstrate LSP-Align outperforms baselines at extracting cross-lingual entity pairs and mine 164 million entity pairs from 120 different languages aligned with English. We release these cross-lingual entity pairs along with the massively multilingual tagged named entity corpus as a resource to the NLP community.

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Quality Estimation without Human-labeled Data
Yi-Lin Tuan | Ahmed El-Kishky | Adithya Renduchintala | Vishrav Chaudhary | Francisco Guzmán | Lucia Specia
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Quality estimation aims to measure the quality of translated content without access to a reference translation. This is crucial for machine translation systems in real-world scenarios where high-quality translation is needed. While many approaches exist for quality estimation, they are based on supervised machine learning requiring costly human labelled data. As an alternative, we propose a technique that does not rely on examples from human-annotators and instead uses synthetic training data. We train off-the-shelf architectures for supervised quality estimation on our synthetic data and show that the resulting models achieve comparable performance to models trained on human-annotated data, both for sentence and word-level prediction.


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CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs
Ahmed El-Kishky | Vishrav Chaudhary | Francisco Guzmán | Philipp Koehn
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. In this paper, we exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5% across different language pairs. We mine sixty-eight snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other. We release a new web dataset consisting of over 392 million URL pairs from Common Crawl covering documents in 8144 language pairs of which 137 pairs include English. In addition to curating this massive dataset, we introduce baseline methods that leverage cross-lingual representations to identify aligned documents based on their textual content. Finally, we demonstrate the value of this parallel documents dataset through a downstream task of mining parallel sentences and measuring the quality of machine translations from models trained on this mined data. Our objective in releasing this dataset is to foster new research in cross-lingual NLP across a variety of low, medium, and high-resource languages.

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Findings of the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment
Philipp Koehn | Vishrav Chaudhary | Ahmed El-Kishky | Naman Goyal | Peng-Jen Chen | Francisco Guzmán
Proceedings of the Fifth Conference on Machine Translation

Following two preceding WMT Shared Task on Parallel Corpus Filtering (Koehn et al., 2018, 2019), we posed again the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting the highest-quality data to be used to train ma-chine translation systems. This year, the task tackled the low resource condition of Pashto–English and Khmer–English and also included the challenge of sentence alignment from document pairs.

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An Exploratory Study on Multilingual Quality Estimation
Shuo Sun | Marina Fomicheva | Frédéric Blain | Vishrav Chaudhary | Ahmed El-Kishky | Adithya Renduchintala | Francisco Guzmán | Lucia Specia
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Predicting the quality of machine translation has traditionally been addressed with language-specific models, under the assumption that the quality label distribution or linguistic features exhibit traits that are not shared across languages. An obvious disadvantage of this approach is the need for labelled data for each given language pair. We challenge this assumption by exploring different approaches to multilingual Quality Estimation (QE), including using scores from translation models. We show that these outperform single-language models, particularly in less balanced quality label distributions and low-resource settings. In the extreme case of zero-shot QE, we show that it is possible to accurately predict quality for any given new language from models trained on other languages. Our findings indicate that state-of-the-art neural QE models based on powerful pre-trained representations generalise well across languages, making them more applicable in real-world settings.

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Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover’s Distance
Ahmed El-Kishky | Francisco Guzmán
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. Such aligned data can be used for a variety of NLP tasks from training cross-lingual representations to mining parallel data for machine translation. In this paper we develop an unsupervised scoring function that leverages cross-lingual sentence embeddings to compute the semantic distance between documents in different languages. These semantic distances are then used to guide a document alignment algorithm to properly pair cross-lingual web documents across a variety of low, mid, and high-resource language pairs. Recognizing that our proposed scoring function and other state of the art methods are computationally intractable for long web documents, we utilize a more tractable greedy algorithm that performs comparably. We experimentally demonstrate that our distance metric performs better alignment than current baselines outperforming them by 7% on high-resource language pairs, 15% on mid-resource language pairs, and 22% on low-resource language pairs.


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Facebook AI’s WAT19 Myanmar-English Translation Task Submission
Peng-Jen Chen | Jiajun Shen | Matthew Le | Vishrav Chaudhary | Ahmed El-Kishky | Guillaume Wenzek | Myle Ott | Marc’Aurelio Ranzato
Proceedings of the 6th Workshop on Asian Translation

This paper describes Facebook AI’s submission to the WAT 2019 Myanmar-English translation task. Our baseline systems are BPE-based transformer models. We explore methods to leverage monolingual data to improve generalization, including self-training, back-translation and their combination. We further improve results by using noisy channel re-ranking and ensembling. We demonstrate that these techniques can significantly improve not only a system trained with additional monolingual data, but even the baseline system trained exclusively on the provided small parallel dataset. Our system ranks first in both directions according to human evaluation and BLEU, with a gain of over 8 BLEU points above the second best system.

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Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings
Ahmed El-Kishky | Xingyu Fu | Aseel Addawood | Nahil Sobh | Clare Voss | Jiawei Han
Proceedings of the Fourth Arabic Natural Language Processing Workshop

In this paper, we tackle the problem of “root extraction” from words in the Semitic language family. A challenge in applying natural language processing techniques to these languages is the data sparsity problem that arises from their rich internal morphology, where the substructure is inherently non-concatenative and morphemes are interdigitated in word formation. While previous automated methods have relied on human-curated rules or multiclass classification, they have not fully leveraged the various combinations of regular, sequential concatenative morphology within the words and the internal interleaving within templatic stems of roots and patterns. To address this, we propose a constrained sequence-to-sequence root extraction method. Experimental results show our constrained model outperforms a variety of methods at root extraction. Furthermore, by enriching word embeddings with resulting decompositions, we show improved results on word analogy, word similarity, and language modeling tasks.


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Entropy-Based Subword Mining with an Application to Word Embeddings
Ahmed El-Kishky | Frank Xu | Aston Zhang | Stephen Macke | Jiawei Han
Proceedings of the Second Workshop on Subword/Character LEvel Models

Recent literature has shown a wide variety of benefits to mapping traditional one-hot representations of words and phrases to lower-dimensional real-valued vectors known as word embeddings. Traditionally, most word embedding algorithms treat each word as the finest meaningful semantic granularity and perform embedding by learning distinct embedding vectors for each word. Contrary to this line of thought, technical domains such as scientific and medical literature compose words from subword structures such as prefixes, suffixes, and root-words as well as compound words. Treating individual words as the finest-granularity unit discards meaningful shared semantic structure between words sharing substructures. This not only leads to poor embeddings for text corpora that have long-tail distributions, but also heuristic methods for handling out-of-vocabulary words. In this paper we propose SubwordMine, an entropy-based subword mining algorithm that is fast, unsupervised, and fully data-driven. We show that this allows for great cross-domain performance in identifying semantically meaningful subwords. We then investigate utilizing the mined subwords within the FastText embedding model and compare performance of the learned representations in a downstream language modeling task.