Saab Mansour


2021

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Nearest Neighbour Few-Shot Learning for Cross-lingual Classification
M Saiful Bari | Batool Haider | Saab Mansour
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Even though large pre-trained multilingual models (e.g. mBERT, XLM-R) have led to significant performance gains on a wide range of cross-lingual NLP tasks, success on many downstream tasks still relies on the availability of sufficient annotated data. Traditional fine-tuning of pre-trained models using only a few target samples can cause over-fitting. This can be quite limiting as most languages in the world are under-resourced. In this work, we investigate cross-lingual adaptation using a simple nearest-neighbor few-shot (<15 samples) inference technique for classification tasks. We experiment using a total of 16 distinct languages across two NLP tasks- XNLI and PAWS-X. Our approach consistently improves traditional fine-tuning using only a handful of labeled samples in target locales. We also demonstrate its generalization capability across tasks.

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ODIST: Open World Classification via Distributionally Shifted Instances
Lei Shu | Yassine Benajiba | Saab Mansour | Yi Zhang
Findings of the Association for Computational Linguistics: EMNLP 2021

In this work, we address the open-world classification problem with a method called ODIST, open world classification via distributionally shifted instances. This novel and straightforward method can create out-of-domain instances from the in-domain training instances with the help of a pre-trained generative language model. Experimental results show that ODIST performs better than state-of-the-art decision boundary finding method.

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Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings
Sawsan Alqahtani | Garima Lalwani | Yi Zhang | Salvatore Romeo | Saab Mansour
Findings of the Association for Computational Linguistics: EMNLP 2021

Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target embedding spaces. For contextualized embeddings, alignment becomes more complex as we additionally take context into consideration. In this work, we propose using Optimal Transport (OT) as an alignment objective during fine-tuning to further improve multilingual contextualized representations for downstream cross-lingual transfer. This approach does not require word-alignment pairs prior to fine-tuning that may lead to sub-optimal matching and instead learns the word alignments within context in an unsupervised manner. It also allows different types of mappings due to soft matching between source and target sentences. We benchmark our proposed method on two tasks (XNLI and XQuAD) and achieve improvements over baselines as well as competitive results compared to similar recent works.

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Soft Layer Selection with Meta-Learning for Zero-Shot Cross-Lingual Transfer
Weijia Xu | Batool Haider | Jason Krone | Saab Mansour
Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing

Multilingual pre-trained contextual embedding models (Devlin et al., 2019) have achieved impressive performance on zero-shot cross-lingual transfer tasks. Finding the most effective fine-tuning strategy to fine-tune these models on high-resource languages so that it transfers well to the zero-shot languages is a non-trivial task. In this paper, we propose a novel meta-optimizer to soft-select which layers of the pre-trained model to freeze during fine-tuning. We train the meta-optimizer by simulating the zero-shot transfer scenario. Results on cross-lingual natural language inference show that our approach improves over the simple fine-tuning baseline and X-MAML (Nooralahzadeh et al., 2020).

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Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems
Piyawat Lertvittayakumjorn | Daniele Bonadiman | Saab Mansour
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In goal-oriented dialogue systems, users provide information through slot values to achieve specific goals. Practically, some combinations of slot values can be invalid according to external knowledge. For example, a combination of “cheese pizza” (a menu item) and “oreo cookies” (a topping) from an input utterance “Can I order a cheese pizza with oreo cookies on top?” exemplifies such invalid combinations according to the menu of a restaurant business. Traditional dialogue systems allow execution of validation rules as a post-processing step after slots have been filled which can lead to error accumulation. In this paper, we formalize knowledge-driven slot constraints and present a new task of constraint violation detection accompanied with benchmarking data. Then, we propose methods to integrate the external knowledge into the system and model constraint violation detection as an end-to-end classification task and compare it to the traditional rule-based pipeline approach. Experiments on two domains of the MultiDoGO dataset reveal challenges of constraint violation detection and sets the stage for future work and improvements.

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On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world Noise
Sailik Sengupta | Jason Krone | Saab Mansour
Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI

Intent Classification (IC) and Slot Labeling (SL) models, which form the basis of dialogue systems, often encounter noisy data in real-word environments. In this work, we investigate how robust IC/SL models are to noisy data. We collect and publicly release a test-suite for seven common noise types found in production human-to-bot conversations (abbreviations, casing, misspellings, morphological variants, paraphrases, punctuation and synonyms). On this test-suite, we show that common noise types substantially degrade the IC accuracy and SL F1 performance of state-of-the-art BERT-based IC/SL models. By leveraging cross-noise robustness transfer, i.e. training on one noise type to improve robustness on another noise type, we design aggregate data-augmentation approaches that increase the model performance across all seven noise types by +10.8% for IC accuracy and +15 points for SL F1 on average. To the best of our knowledge, this is the first work to present a single IC/SL model that is robust to a wide range of noise phenomena.

2020

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End-to-End Slot Alignment and Recognition for Cross-Lingual NLU
Weijia Xu | Batool Haider | Saab Mansour
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Natural language understanding (NLU) in the context of goal-oriented dialog systems typically includes intent classification and slot labeling tasks. Existing methods to expand an NLU system to new languages use machine translation with slot label projection from source to the translated utterances, and thus are sensitive to projection errors. In this work, we propose a novel end-to-end model that learns to align and predict target slot labels jointly for cross-lingual transfer. We introduce MultiATIS++, a new multilingual NLU corpus that extends the Multilingual ATIS corpus to nine languages across four language families, and evaluate our method using the corpus. Results show that our method outperforms a simple label projection method using fast-align on most languages, and achieves competitive performance to the more complex, state-of-the-art projection method with only half of the training time. We release our MultiATIS++ corpus to the community to continue future research on cross-lingual NLU.

2015

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Spelling Correction of User Search Queries through Statistical Machine Translation
Saša Hasan | Carmen Heger | Saab Mansour
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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Unsupervised Adaptation for Statistical Machine Translation
Saab Mansour | Hermann Ney
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Improved Sentence-Level Arabic Dialect Classification
Christoph Tillmann | Saab Mansour | Yaser Al-Onaizan
Proceedings of the First Workshop on Applying NLP Tools to Similar Languages, Varieties and Dialects

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Automatic dialect classification for statistical machine translation
Saab Mansour | Yaser Al-Onaizan | Graeme Blackwood | Christoph Tillmann
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

The training data for statistical machine translation are gathered from various sources representing a mixture of domains. In this work, we argue that when translating dialects representing varieties of the same language, a manually assigned data source is not a reliable indicator of the dialect. We resort to automatic dialect classification to refine the training corpora according to the different dialects and build improved dialect specific systems. A fairly standard classifier for Arabic developed within this work achieves state-of-the-art performance, with classification precision above 90%, making it usefully accurate for our application. The classification of the data is then used to distinguish between the different dialects, split the data accordingly, and utilize the new splits for several adaptation techniques. Performing translation experiments on a large scale dialectal Arabic to English translation task, our results show that the classifier generates better contrast between the dialects and achieves superior translation quality than using the original manual corpora splits.

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Translation model based weighting for phrase extraction
Saab Mansour | Herman Ney
Proceedings of the 17th Annual conference of the European Association for Machine Translation

2013

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Phrase Training Based Adaptation for Statistical Machine Translation
Saab Mansour | Hermann Ney
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Joint WMT 2013 Submission of the QUAERO Project
Stephan Peitz | Saab Mansour | Matthias Huck | Markus Freitag | Hermann Ney | Eunah Cho | Teresa Herrmann | Mohammed Mediani | Jan Niehues | Alex Waibel | Alexander Allauzen | Quoc Khanh Do | Bianka Buschbeck | Tonio Wandmacher
Proceedings of the Eighth Workshop on Statistical Machine Translation

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The RWTH Aachen Machine Translation System for WMT 2013
Stephan Peitz | Saab Mansour | Jan-Thorsten Peter | Christoph Schmidt | Joern Wuebker | Matthias Huck | Markus Freitag | Hermann Ney
Proceedings of the Eighth Workshop on Statistical Machine Translation

2012

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Jane 2: Open Source Phrase-based and Hierarchical Statistical Machine Translation
Joern Wuebker | Matthias Huck | Stephan Peitz | Malte Nuhn | Markus Freitag | Jan-Thorsten Peter | Saab Mansour | Hermann Ney
Proceedings of COLING 2012: Demonstration Papers

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The RWTH Aachen speech recognition and machine translation system for IWSLT 2012
Stephan Peitz | Saab Mansour | Markus Freitag | Minwei Feng | Matthias Huck | Joern Wuebker | Malte Nuhn | Markus Nußbaum-Thom | Hermann Ney
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, the automatic speech recognition (ASR) and statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2012 are presented. We participated in the ASR (English), MT (English-French, Arabic-English, Chinese-English, German-English) and SLT (English-French) tracks. For the MT track both hierarchical and phrase-based SMT decoders are applied. A number of different techniques are evaluated in the MT and SLT tracks, including domain adaptation via data selection, translation model interpolation, phrase training for hierarchical and phrase-based systems, additional reordering model, word class language model, various Arabic and Chinese segmentation methods, postprocessing of speech recognition output with an SMT system, and system combination. By application of these methods we can show considerable improvements over the respective baseline systems.

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A simple and effective weighted phrase extraction for machine translation adaptation
Saab Mansour | Hermann Ney
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

The task of domain-adaptation attempts to exploit data mainly drawn from one domain (e.g. news) to maximize the performance on the test domain (e.g. weblogs). In previous work, weighting the training instances was used for filtering dissimilar data. We extend this by incorporating the weights directly into the standard phrase training procedure of statistical machine translation (SMT). This allows the SMT system to make the decision whether to use a phrase translation pair or not, a more methodological way than discarding phrase pairs completely when using filtering. Furthermore, we suggest a combined filtering and weighting procedure to achieve better results while reducing the phrase table size. The proposed methods are evaluated in the context of Arabicto-English translation on various conditions, where significant improvements are reported when using the suggested weighted phrase training. The weighting method also improves over filtering, and the combined filtering and weighting is better than a standalone filtering method. Finally, we experiment with mixture modeling, where additional improvements are reported when using weighted phrase extraction over a variety of baselines.

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Arabic-Segmentation Combination Strategies for Statistical Machine Translation
Saab Mansour | Hermann Ney
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Arabic segmentation was already applied successfully for the task of statistical machine translation (SMT). Yet, there is no consistent comparison of the effect of different techniques and methods over the final translation quality. In this work, we use existing tools and further re-implement and develop new methods for segmentation. We compare the resulting SMT systems based on the different segmentation methods over the small IWSLT 2010 BTEC and the large NIST 2009 Arabic-to-English translation tasks. Our results show that for both small and large training data, segmentation yields strong improvements, but, the differences between the top ranked segmenters are statistically insignificant. Due to the different methodologies that we apply for segmentation, we expect a complimentary variation in the results achieved by each method. As done in previous work, we combine several segmentation schemes of the same model but achieve modest improvements. Next, we try a different strategy, where we combine the different segmentation methods rather than the different segmentation schemes. In this case, we achieve stronger improvements over the best single system. Finally, combining schemes and methods has another slight gain over the best combination strategy.

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A Holistic Approach to Bilingual Sentence Fragment Extraction from Comparable Corpora
Mahdi Khademian | Kaveh Taghipour | Saab Mansour | Shahram Khadivi
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Achieving accurate translation, especially in multiple domain documents with statistical machine translation systems, requires more and more bilingual texts and this need becomes more critical when training such systems for language pairs with scarce training data. In the recent years, there have been some researches on new sources of parallel texts that are documents which are not necessarily parallel but are comparable. Since these methods search for possible translation equivalences in a greedy manner, they are unable to consider all possible parallel texts in comparable documents. This paper investigates a different approach for this need by considering relationships between all words of two comparable documents, which works fairly well even in the worst case of comparability. We represent each document pair in a matrix and then transform it to a new space to find parallel fragments. Evaluations show that the system is successful in extraction of useful fragment pairs.

2011

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The RWTH Aachen Machine Translation System for WMT 2011
Matthias Huck | Joern Wuebker | Christoph Schmidt | Markus Freitag | Stephan Peitz | Daniel Stein | Arnaud Dagnelies | Saab Mansour | Gregor Leusch | Hermann Ney
Proceedings of the Sixth Workshop on Statistical Machine Translation

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The RWTH Aachen machine translation system for IWSLT 2011
Joern Wuebker | Matthias Huck | Saab Mansour | Markus Freitag | Minwei Feng | Stephan Peitz | Christoph Schmidt | Hermann Ney
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2011 is presented. We participated in the MT (English-French, Arabic-English, ChineseEnglish) and SLT (English-French) tracks. Both hierarchical and phrase-based SMT decoders are applied. A number of different techniques are evaluated, including domain adaptation via monolingual and bilingual data selection, phrase training, different lexical smoothing methods, additional reordering models for the hierarchical system, various Arabic and Chinese segmentation methods, punctuation prediction for speech recognition output, and system combination. By application of these methods we can show considerable improvements over the respective baseline systems.

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Lexicon models for hierarchical phrase-based machine translation
Matthias Huck | Saab Mansour | Simon Wiesler | Hermann Ney
Proceedings of the 8th International Workshop on Spoken Language Translation: Papers

In this paper, we investigate lexicon models for hierarchical phrase-based statistical machine translation. We study five types of lexicon models: a model which is extracted from word-aligned training data and—given the word alignment matrix—relies on pure relative frequencies [1]; the IBM model 1 lexicon [2]; a regularized version of IBM model 1; a triplet lexicon model variant [3]; and a discriminatively trained word lexicon model [4]. We explore sourceto-target models with phrase-level as well as sentence-level scoring and target-to-source models with scoring on phrase level only. For the first two types of lexicon models, we compare several scoring variants. All models are used during search, i.e. they are incorporated directly into the log-linear model combination of the decoder. Phrase table smoothing with triplet lexicon models and with discriminative word lexicons are novel contributions. We also propose a new regularization technique for IBM model 1 by means of the Kullback-Leibler divergence with the empirical unigram distribution as regularization term. Experiments are carried out on the large-scale NIST Chinese→English translation task and on the English→French and Arabic→English IWSLT TED tasks. For Chinese→English and English→French, we obtain the best results by using the discriminative word lexicon to smooth our phrase tables.

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Combining translation and language model scoring for domain-specific data filtering
Saab Mansour | Joern Wuebker | Hermann Ney
Proceedings of the 8th International Workshop on Spoken Language Translation: Papers

The increasing popularity of statistical machine translation (SMT) systems is introducing new domains of translation that need to be tackled. As many resources are already available, domain adaptation methods can be applied to utilize these recourses in the most beneficial way for the new domain. We explore adaptation via filtering, using the crossentropy scores to discard irrelevant sentences. We focus on filtering for two important components of an SMT system, namely the language model (LM) and the translation model (TM). Previous work has already applied LM cross-entropy based scoring for filtering. We argue that LM cross-entropy might be appropriate for LM filtering, but not as much for TM filtering. We develop a novel filtering approach based on a combined TM and LM cross-entropy scores. We experiment with two large-scale translation tasks, the Arabic-to-English and English-to-French IWSLT 2011 TED Talks MT tasks. For LM filtering, we achieve strong perplexity improvements which carry over to the translation quality with improvements up to +0.4% BLEU. For TM filtering, the combined method achieves small but consistent improvements over the standalone methods. As a side effect of adaptation via filtering, the fully fledged SMT system vocabulary size and phrase table size are reduced by a factor of at least 2 while up to +0.6% BLEU improvement is observed.

2010

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The RWTH Aachen machine translation system for IWSLT 2010
Saab Mansour | Stephan Peitz | David Vilar | Joern Wuebker | Hermann Ney
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper we describe the statistical machine translation system of the RWTH Aachen University developed for the translation task of the IWSLT 2010. This year, we participated in the BTEC translation task for the Arabic to English language direction. We experimented with two state-of-theart decoders: phrase-based and hierarchical-based decoders. Extensions to the decoders included phrase training (as opposed to heuristic phrase extraction) for the phrase-based decoder, and soft syntactic features for the hierarchical decoder. Additionally, we experimented with various rule-based and statistical-based segmenters for Arabic. Due to the different decoders and the different methodologies that we apply for segmentation, we expect that there will be complimentary variation in the results achieved by each system. The next step would be to exploit these variations and achieve better results by combining the systems. We try different strategies for system combination and report significant improvements over the best single system.

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MorphTagger: HMM-based Arabic segmentation for statistical machine translation
Saab Mansour
Proceedings of the 7th International Workshop on Spoken Language Translation: Papers

In this paper, we investigate different methodologies of Arabic segmentation for statistical machine translation by comparing a rule-based segmenter to different statistically-based segmenters. We also present a new method for segmentation that serves the need for a real-time translation system without impairing the translation accuracy.

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The RWTH Aachen Machine Translation System for WMT 2010
Carmen Heger | Joern Wuebker | Matthias Huck | Gregor Leusch | Saab Mansour | Daniel Stein | Hermann Ney
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

2008

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The RWTH machine translation system for IWSLT 2008.
David Vilar | Daniel Stein | Yuqi Zhang | Evgeny Matusov | Arne Mauser | Oliver Bender | Saab Mansour | Hermann Ney
Proceedings of the 5th International Workshop on Spoken Language Translation: Evaluation Campaign

RWTH’s system for the 2008 IWSLT evaluation consists of a combination of different phrase-based and hierarchical statistical machine translation systems. We participated in the translation tasks for the Chinese-to-English and Arabic-to-English language pairs. We investigated different preprocessing techniques, reordering methods for the phrase-based system, including reordering of speech lattices, and syntax-based enhancements for the hierarchical systems. We also tried the combination of the Arabic-to-English and Chinese-to-English outputs as an additional submission.