Stella Frank


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Challenges and Strategies in Cross-Cultural NLP
Daniel Hershcovich | Stella Frank | Heather Lent | Miryam de Lhoneux | Mostafa Abdou | Stephanie Brandl | Emanuele Bugliarello | Laura Cabello Piqueras | Ilias Chalkidis | Ruixiang Cui | Constanza Fierro | Katerina Margatina | Phillip Rust | Anders Søgaard
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages. However, it is important to acknowledge that speakers and the content they produce and require, vary not just by language, but also by culture. Although language and culture are tightly linked, there are important differences. Analogous to cross-lingual and multilingual NLP, cross-cultural and multicultural NLP considers these differences in order to better serve users of NLP systems. We propose a principled framework to frame these efforts, and survey existing and potential strategies.

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Multilingual Multimodal Learning with Machine Translated Text
Chen Qiu | Dan Oneață | Emanuele Bugliarello | Stella Frank | Desmond Elliott
Findings of the Association for Computational Linguistics: EMNLP 2022

Most vision-and-language pretraining research focuses on English tasks. However, the creation of multilingual multimodal evaluation datasets (e.g. Multi30K, xGQA, XVNLI, and MaRVL) poses a new challenge in finding high-quality training data that is both multilingual and multimodal. In this paper, we investigate whether machine translating English multimodal data can be an effective proxy for the lack of readily available multilingual data. We call this framework TD-MML: Translated Data for Multilingual Multimodal Learning, and it can be applied to any multimodal dataset and model. We apply it to both pretraining and fine-tuning data with a state-of-the-art model. In order to prevent models from learning from low-quality translated text, we propose two metrics for automatically removing such translations from the resulting datasets. In experiments on five tasks across 20 languages in the IGLUE benchmark, we show that translated data can provide a useful signal for multilingual multimodal learning, both at pretraining and fine-tuning.

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A Small but Informed and Diverse Model: The Case of the Multimodal GuessWhat!? Guessing Game
Claudio Greco | Alberto Testoni | Raffaella Bernardi | Stella Frank
Proceedings of the 2022 CLASP Conference on (Dis)embodiment

Pre-trained Vision and Language Transformers achieve high performance on downstream tasks due to their ability to transfer representational knowledge accumulated during pretraining on substantial amounts of data. In this paper, we ask whether it is possible to compete with such models using features based on transferred (pre-trained, frozen) representations combined with a lightweight architecture. We take a multimodal guessing task as our testbed, GuessWhat?!. An ensemble of our lightweight model matches the performance of the finetuned pre-trained transformer (LXMERT). An uncertainty analysis of our ensemble shows that the lightweight transferred representations close the data uncertainty gap with LXMERT, while retaining model diversity leading to ensemble boost. We further demonstrate that LXMERT’s performance gain is due solely to its extra V&L pretraining rather than because of architectural improvements. These results argue for flexible integration of multiple features and lightweight models as a viable alternative to large, cumbersome, pre-trained models.


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Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color
Mostafa Abdou | Artur Kulmizev | Daniel Hershcovich | Stella Frank | Ellie Pavlick | Anders Søgaard
Proceedings of the 25th Conference on Computational Natural Language Learning

Pretrained language models have been shown to encode relational information, such as the relations between entities or concepts in knowledge-bases — (Paris, Capital, France). However, simple relations of this type can often be recovered heuristically and the extent to which models implicitly reflect topological structure that is grounded in world, such as perceptual structure, is unknown. To explore this question, we conduct a thorough case study on color. Namely, we employ a dataset of monolexemic color terms and color chips represented in CIELAB, a color space with a perceptually meaningful distance metric. Using two methods of evaluating the structural alignment of colors in this space with text-derived color term representations, we find significant correspondence. Analyzing the differences in alignment across the color spectrum, we find that warmer colors are, on average, better aligned to the perceptual color space than cooler ones, suggesting an intriguing connection to findings from recent work on efficient communication in color naming. Further analysis suggests that differences in alignment are, in part, mediated by collocationality and differences in syntactic usage, posing questions as to the relationship between color perception and usage and context.

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Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal Transformers
Stella Frank | Emanuele Bugliarello | Desmond Elliott
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Pretrained vision-and-language BERTs aim to learn representations that combine information from both modalities. We propose a diagnostic method based on cross-modal input ablation to assess the extent to which these models actually integrate cross-modal information. This method involves ablating inputs from one modality, either entirely or selectively based on cross-modal grounding alignments, and evaluating the model prediction performance on the other modality. Model performance is measured by modality-specific tasks that mirror the model pretraining objectives (e.g. masked language modelling for text). Models that have learned to construct cross-modal representations using both modalities are expected to perform worse when inputs are missing from a modality. We find that recently proposed models have much greater relative difficulty predicting text when visual information is ablated, compared to predicting visual object categories when text is ablated, indicating that these models are not symmetrically cross-modal.


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CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language Learning
Alessandro Suglia | Ioannis Konstas | Andrea Vanzo | Emanuele Bastianelli | Desmond Elliott | Stella Frank | Oliver Lemon
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Approaches to Grounded Language Learning are commonly focused on a single task-based final performance measure which may not depend on desirable properties of the learned hidden representations, such as their ability to predict object attributes or generalize to unseen situations. To remedy this, we present GroLLA, an evaluation framework for Grounded Language Learning with Attributes based on three sub-tasks: 1) Goal-oriented evaluation; 2) Object attribute prediction evaluation; and 3) Zero-shot evaluation. We also propose a new dataset CompGuessWhat?! as an instance of this framework for evaluating the quality of learned neural representations, in particular with respect to attribute grounding. To this end, we extend the original GuessWhat?! dataset by including a semantic layer on top of the perceptual one. Specifically, we enrich the VisualGenome scene graphs associated with the GuessWhat?! images with several attributes from resources such as VISA and ImSitu. We then compare several hidden state representations from current state-of-the-art approaches to Grounded Language Learning. By using diagnostic classifiers, we show that current models’ learned representations are not expressive enough to encode object attributes (average F1 of 44.27). In addition, they do not learn strategies nor representations that are robust enough to perform well when novel scenes or objects are involved in gameplay (zero-shot best accuracy 50.06%).


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Findings of the Third Shared Task on Multimodal Machine Translation
Loïc Barrault | Fethi Bougares | Lucia Specia | Chiraag Lala | Desmond Elliott | Stella Frank
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We present the results from the third shared task on multimodal machine translation. In this task a source sentence in English is supplemented by an image and participating systems are required to generate a translation for such a sentence into German, French or Czech. The image can be used in addition to (or instead of) the source sentence. This year the task was extended with a third target language (Czech) and a new test set. In addition, a variant of this task was introduced with its own test set where the source sentence is given in multiple languages: English, French and German, and participating systems are required to generate a translation in Czech. Seven teams submitted 45 different systems to the two variants of the task. Compared to last year, the performance of the multimodal submissions improved, but text-only systems remain competitive.


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Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description
Desmond Elliott | Stella Frank | Loïc Barrault | Fethi Bougares | Lucia Specia
Proceedings of the Second Conference on Machine Translation


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The QT21/HimL Combined Machine Translation System
Jan-Thorsten Peter | Tamer Alkhouli | Hermann Ney | Matthias Huck | Fabienne Braune | Alexander Fraser | Aleš Tamchyna | Ondřej Bojar | Barry Haddow | Rico Sennrich | Frédéric Blain | Lucia Specia | Jan Niehues | Alex Waibel | Alexandre Allauzen | Lauriane Aufrant | Franck Burlot | Elena Knyazeva | Thomas Lavergne | François Yvon | Mārcis Pinnis | Stella Frank
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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ILLC-UvA Adaptation System (Scorpio) at WMT’16 IT-DOMAIN Task
Hoang Cuong | Stella Frank | Khalil Sima’an
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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A Shared Task on Multimodal Machine Translation and Crosslingual Image Description
Lucia Specia | Stella Frank | Khalil Sima’an | Desmond Elliott
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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DCU-UvA Multimodal MT System Report
Iacer Calixto | Desmond Elliott | Stella Frank
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Multi30K: Multilingual English-German Image Descriptions
Desmond Elliott | Stella Frank | Khalil Sima’an | Lucia Specia
Proceedings of the 5th Workshop on Vision and Language


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Splitting Compounds by Semantic Analogy
Joachim Daiber | Lautaro Quiroz | Roger Wechsler | Stella Frank
Proceedings of the 1st Deep Machine Translation Workshop


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Learning the hyperparameters to learn morphology
Stella Frank
Proceedings of the 5th Workshop on Cognitive Aspects of Computational Language Learning (CogACLL)

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Weak semantic context helps phonetic learning in a model of infant language acquisition
Stella Frank | Naomi H. Feldman | Sharon Goldwater
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Exploring the Utility of Joint Morphological and Syntactic Learning from Child-directed Speech
Stella Frank | Frank Keller | Sharon Goldwater
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing


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Using Sentence Type Information for Syntactic Category Acquisition
Stella Frank | Sharon Goldwater | Frank Keller
Proceedings of the 2010 Workshop on Cognitive Modeling and Computational Linguistics