Georgiana Dinu

Also published as: G. Dinu


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RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation
Gabriele Sarti | Phu Mon Htut | Xing Niu | Benjamin Hsu | Anna Currey | Georgiana Dinu | Maria Nadejde
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Attribute-controlled translation (ACT) is a subtask of machine translation that involves controlling stylistic or linguistic attributes (like formality and gender) of translation outputs. While ACT has garnered attention in recent years due to its usefulness in real-world applications, progress in the task is currently limited by dataset availability, since most prior approaches rely on supervised methods. To address this limitation, we propose Retrieval and Attribute-Marking enhanced Prompting (RAMP), which leverages large multilingual language models to perform ACT in few-shot and zero-shot settings. RAMP improves generation accuracy over the standard prompting approach by (1) incorporating a semantic similarity retrieval component for selecting similar in-context examples, and (2) marking in-context examples with attribute annotations. Our comprehensive experiments show that RAMP is a viable approach in both zero-shot and few-shot settings.


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Findings of the IWSLT 2022 Evaluation Campaign
Antonios Anastasopoulos | Loïc Barrault | Luisa Bentivogli | Marcely Zanon Boito | Ondřej Bojar | Roldano Cattoni | Anna Currey | Georgiana Dinu | Kevin Duh | Maha Elbayad | Clara Emmanuel | Yannick Estève | Marcello Federico | Christian Federmann | Souhir Gahbiche | Hongyu Gong | Roman Grundkiewicz | Barry Haddow | Benjamin Hsu | Dávid Javorský | Vĕra Kloudová | Surafel Lakew | Xutai Ma | Prashant Mathur | Paul McNamee | Kenton Murray | Maria Nǎdejde | Satoshi Nakamura | Matteo Negri | Jan Niehues | Xing Niu | John Ortega | Juan Pino | Elizabeth Salesky | Jiatong Shi | Matthias Sperber | Sebastian Stüker | Katsuhito Sudoh | Marco Turchi | Yogesh Virkar | Alexander Waibel | Changhan Wang | Shinji Watanabe
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation. A total of 27 teams participated in at least one of the shared tasks. This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved.

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CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality
Maria Nadejde | Anna Currey | Benjamin Hsu | Xing Niu | Marcello Federico | Georgiana Dinu
Findings of the Association for Computational Linguistics: NAACL 2022

The machine translation (MT) task is typically formulated as that of returning a single translation for an input segment. However, in many cases, multiple different translations are valid and the appropriate translation may depend on the intended target audience, characteristics of the speaker, or even the relationship between speakers. Specific problems arise when dealing with honorifics, particularly translating from English into languages with formality markers. For example, the sentence “Are you sure?” can be translated in German as “Sind Sie sich sicher?” (formal register) or “Bist du dir sicher?” (informal). Using wrong or inconsistent tone may be perceived as inappropriate or jarring for users of certain cultures and demographics. This work addresses the problem of learning to control target language attributes, in this case formality, from a small amount of labeled contrastive data. We introduce an annotated dataset (CoCoA-MT) and an associated evaluation metric for training and evaluating formality-controlled MT models for six diverse target languages. We show that we can train formality-controlled models by fine-tuning on labeled contrastive data, achieving high accuracy (82% in-domain and 73% out-of-domain) while maintaining overall quality.

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MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation
Anna Currey | Maria Nadejde | Raghavendra Reddy Pappagari | Mia Mayer | Stanislas Lauly | Xing Niu | Benjamin Hsu | Georgiana Dinu
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased. In particular, gender accuracy in translation can have implications in terms of output fluency, translation accuracy, and ethics. In this paper, we introduce MT-GenEval, a benchmark for evaluating gender accuracy in translation from English into eight widely-spoken languages. MT-GenEval complements existing benchmarks by providing realistic, gender-balanced, counterfactual data in eight language pairs where the gender of individuals is unambiguous in the input segment, including multi-sentence segments requiring inter-sentential gender agreement. Our data and code is publicly available under a CC BY SA 3.0 license.


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Findings of the WMT Shared Task on Machine Translation Using Terminologies
Md Mahfuz Ibn Alam | Ivana Kvapilíková | Antonios Anastasopoulos | Laurent Besacier | Georgiana Dinu | Marcello Federico | Matthias Gallé | Kweonwoo Jung | Philipp Koehn | Vassilina Nikoulina
Proceedings of the Sixth Conference on Machine Translation

Language domains that require very careful use of terminology are abundant and reflect a significant part of the translation industry. In this work we introduce a benchmark for evaluating the quality and consistency of terminology translation, focusing on the medical (and COVID-19 specifically) domain for five language pairs: English to French, Chinese, Russian, and Korean, as well as Czech to German. We report the descriptions and results of the participating systems, commenting on the need for further research efforts towards both more adequate handling of terminologies as well as towards a proper formulation and evaluation of the task.

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GFST: Gender-Filtered Self-Training for More Accurate Gender in Translation
Prafulla Kumar Choubey | Anna Currey | Prashant Mathur | Georgiana Dinu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Targeted evaluations have found that machine translation systems often output incorrect gender in translations, even when the gender is clear from context. Furthermore, these incorrectly gendered translations have the potential to reflect or amplify social biases. We propose gender-filtered self-training (GFST) to improve gender translation accuracy on unambiguously gendered inputs. Our GFST approach uses a source monolingual corpus and an initial model to generate gender-specific pseudo-parallel corpora which are then filtered and added to the training data. We evaluate GFST on translation from English into five languages, finding that it improves gender accuracy without damaging generic quality. We also show the viability of GFST on several experimental settings, including re-training from scratch, fine-tuning, controlling the gender balance of the data, forward translation, and back-translation.


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How Should Markup Tags Be Translated?
Greg Hanneman | Georgiana Dinu
Proceedings of the Fifth Conference on Machine Translation

The ability of machine translation (MT) models to correctly place markup is crucial to generating high-quality translations of formatted input. This paper compares two commonly used methods of representing markup tags and tests the ability of MT models to learn tag placement via training data augmentation. We study the interactions of tag representation, data augmentation size, tag complexity, and language pair to show the drawbacks and benefits of each method. We construct and release new test sets containing tagged data for three language pairs of varying difficulty.

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Evaluating Robustness to Input Perturbations for Neural Machine Translation
Xing Niu | Prashant Mathur | Georgiana Dinu | Yaser Al-Onaizan
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Neural Machine Translation (NMT) models are sensitive to small perturbations in the input. Robustness to such perturbations is typically measured using translation quality metrics such as BLEU on the noisy input. This paper proposes additional metrics which measure the relative degradation and changes in translation when small perturbations are added to the input. We focus on a class of models employing subword regularization to address robustness and perform extensive evaluations of these models using the robustness measures proposed. Results show that our proposed metrics reveal a clear trend of improved robustness to perturbations when subword regularization methods are used.

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Joint Translation and Unit Conversion for End-to-end Localization
Georgiana Dinu | Prashant Mathur | Marcello Federico | Stanislas Lauly | Yaser Al-Onaizan
Proceedings of the 17th International Conference on Spoken Language Translation

A variety of natural language tasks require processing of textual data which contains a mix of natural language and formal languages such as mathematical expressions. In this paper, we take unit conversions as an example and propose a data augmentation technique which lead to models learning both translation and conversion tasks as well as how to adequately switch between them for end-to-end localization.

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Distilling Multiple Domains for Neural Machine Translation
Anna Currey | Prashant Mathur | Georgiana Dinu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Neural machine translation achieves impressive results in high-resource conditions, but performance often suffers when the input domain is low-resource. The standard practice of adapting a separate model for each domain of interest does not scale well in practice from both a quality perspective (brittleness under domain shift) as well as a cost perspective (added maintenance and inference complexity). In this paper, we propose a framework for training a single multi-domain neural machine translation model that is able to translate several domains without increasing inference time or memory usage. We show that this model can improve translation on both high- and low-resource domains over strong multi-domain baselines. In addition, our proposed model is effective when domain labels are unknown during training, as well as robust under noisy data conditions.


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Training Neural Machine Translation to Apply Terminology Constraints
Georgiana Dinu | Prashant Mathur | Marcello Federico | Yaser Al-Onaizan
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

This paper proposes a novel method to inject custom terminology into neural machine translation at run time. Previous works have mainly proposed modifications to the decoding algorithm in order to constrain the output to include run-time-provided target terms. While being effective, these constrained decoding methods add, however, significant computational overhead to the inference step, and, as we show in this paper, can be brittle when tested in realistic conditions. In this paper we approach the problem by training a neural MT system to learn how to use custom terminology when provided with the input. Comparative experiments show that our method is not only more effective than a state-of-the-art implementation of constrained decoding, but is also as fast as constraint-free decoding.


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Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP
Georgiana Dinu | Miguel Ballesteros | Avirup Sil | Sam Bowman | Wael Hamza | Anders Sogaard | Tahira Naseem | Yoav Goldberg
Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP


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Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection
Jian Ni | Georgiana Dinu | Radu Florian
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. However, annotating NER data by human is expensive and time-consuming, and can be quite difficult for a new language. In this paper, we present two weakly supervised approaches for cross-lingual NER with no human annotation in a target language. The first approach is to create automatically labeled NER data for a target language via annotation projection on comparable corpora, where we develop a heuristic scheme that effectively selects good-quality projection-labeled data from noisy data. The second approach is to project distributed representations of words (word embeddings) from a target language to a source language, so that the source-language NER system can be applied to the target language without re-training. We also design two co-decoding schemes that effectively combine the outputs of the two projection-based approaches. We evaluate the performance of the proposed approaches on both in-house and open NER data for several target languages. The results show that the combined systems outperform three other weakly supervised approaches on the CoNLL data.


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From Visual Attributes to Adjectives through Decompositional Distributional Semantics
Angeliki Lazaridou | Georgiana Dinu | Adam Liska | Marco Baroni
Transactions of the Association for Computational Linguistics, Volume 3

As automated image analysis progresses, there is increasing interest in richer linguistic annotation of pictures, with attributes of objects (e.g., furry, brown…) attracting most attention. By building on the recent “zero-shot learning” approach, and paying attention to the linguistic nature of attributes as noun modifiers, and specifically adjectives, we show that it is possible to tag images with attribute-denoting adjectives even when no training data containing the relevant annotation are available. Our approach relies on two key observations. First, objects can be seen as bundles of attributes, typically expressed as adjectival modifiers (a dog is something furry, brown, etc.), and thus a function trained to map visual representations of objects to nominal labels can implicitly learn to map attributes to adjectives. Second, objects and attributes come together in pictures (the same thing is a dog and it is brown). We can thus achieve better attribute (and object) label retrieval by treating images as “visual phrases”, and decomposing their linguistic representation into an attribute-denoting adjective and an object-denoting noun. Our approach performs comparably to a method exploiting manual attribute annotation, it out-performs various competitive alternatives in both attribute and object annotation, and it automatically constructs attribute-centric representations that significantly improve performance in supervised object recognition.

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Hubness and Pollution: Delving into Cross-Space Mapping for Zero-Shot Learning
Angeliki Lazaridou | Georgiana Dinu | Marco Baroni
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)


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Improving the Lexical Function Composition Model with Pathwise Optimized Elastic-Net Regression
Jiming Li | Marco Baroni | Georgiana Dinu
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors
Marco Baroni | Georgiana Dinu | Germán Kruszewski
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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How to make words with vectors: Phrase generation in distributional semantics
Georgiana Dinu | Marco Baroni
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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New Directions in Vector Space Models of Meaning
Edward Grefenstette | Karl Moritz Hermann | Georgiana Dinu | Phil Blunsom
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: Tutorials


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A relatedness benchmark to test the role of determiners in compositional distributional semantics
Raffaella Bernardi | Georgiana Dinu | Marco Marelli | Marco Baroni
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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DISSECT - DIStributional SEmantics Composition Toolkit
Georgiana Dinu | Nghia The Pham | Marco Baroni
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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Multi-Step Regression Learning for Compositional Distributional Semantics
E. Grefenstette | G. Dinu | Y. Zhang | M. Sadrzadeh | M. Baroni
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers

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General estimation and evaluation of compositional distributional semantic models
Georgiana Dinu | Nghia The Pham | Marco Baroni
Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality


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Saarland: Vector-based models of semantic textual similarity
Georgiana Dinu | Stefan Thater
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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A comparison of models of word meaning in context
Georgiana Dinu | Stefan Thater | Soeren Laue
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Topic Models for Meaning Similarity in Context
Georgiana Dinu | Mirella Lapata
Coling 2010: Posters

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Measuring Distributional Similarity in Context
Georgiana Dinu | Mirella Lapata
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Relatedness Curves for Acquiring Paraphrases
Georgiana Dinu | Grzegorz Chrupała
Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics


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Ranking Paraphrases in Context
Stefan Thater | Georgiana Dinu | Manfred Pinkal
Proceedings of the 2009 Workshop on Applied Textual Inference (TextInfer)

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Inference Rules for Recognizing Textual Entailment
Georgiana Dinu | Rui Wang
Proceedings of the Eight International Conference on Computational Semantics

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Inference Rules and their Application to Recognizing Textual Entailment
Georgiana Dinu | Rui Wang
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)


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Learning Morphology with Morfette
Grzegorz Chrupala | Georgiana Dinu | Josef van Genabith
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Morfette is a modular, data-driven, probabilistic system which learns to perform joint morphological tagging and lemmatization from morphologically annotated corpora. The system is composed of two learning modules which are trained to predict morphological tags and lemmas using the Maximum Entropy classifier. The third module dynamically combines the predictions of the Maximum-Entropy models and outputs a probability distribution over tag-lemma pair sequences. The lemmatization module exploits the idea of recasting lemmatization as a classification task by using class labels which encode mappings from word forms to lemmas. Experimental evaluation results and error analysis on three morphologically rich languages show that the system achieves high accuracy with no language-specific feature engineering or additional resources.