Papers by Stella Frank
Seeing What Tastes Good: Revisiting Multimodal Distributional Semantics in the Billion Parameter Era (2025.findings-acl)
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| Challenge: | danoneata, et al., 2021): human learning and conceptual representation is grounded in sensorimotor experience. |
| Approach: | They evaluate image encoders and language-only models to learn which attributes are salient to the models. |
| Outcome: | The proposed models outperform language-only models on attributes predicting extended denser McRae norms and newer Binder datasets. |
VISaGE: Understanding Visual Generics and Exceptions (2025.emnlp-main)
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| Challenge: | atypical evaluation instances disrupt incontext instance understanding and in-weight conceptual knowledge. |
| Approach: | They propose to use a dataset to analyze atypical visual and textual images to test their models. |
| Outcome: | The proposed model is based on a dataset consisting of typical and exceptional images. |
Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal Transformers (2021.emnlp-main)
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| Challenge: | Pretrained vision-and-language BERTs aim to learn representations that combine information from both modalities. |
| Approach: | They propose a diagnostic method based on cross-modal input ablation to assess the extent to which pretrained models integrate cross-module information. |
| Outcome: | The proposed method evaluates the model's performance on the other modality based on inputs from one or both modality. |
Challenges and Strategies in Cross-Cultural NLP (2022.acl-long)
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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
| Challenge: | Various efforts have been made to accommodate linguistic diversity and serve speakers of many different languages. |
| Approach: | They propose a framework to examine cultural differences in NLP to better serve users . they argue that cultural knowledge, preferences and values can affect NLP practices . |
| Outcome: | The proposed framework examines how cultural knowledge, preferences and values can affect NLP practices. |
CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language Learning (2020.acl-main)
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Alessandro Suglia, Ioannis Konstas, Andrea Vanzo, Emanuele Bastianelli, Desmond Elliott, Stella Frank, Oliver Lemon
| Challenge: | Approaches to Grounded Language Learning focus on a single task-based final performance measure which may not depend on desirable properties of the learned hidden representations. |
| Approach: | They propose 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. |
| Outcome: | The proposed framework evaluates the quality of learned representations with respect to attribute grounding. |
Multilingual Multimodal Learning with Machine Translated Text (2022.findings-emnlp)
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| Challenge: | Currently, most vision-and-language pretraining research focuses on English tasks due to the availability of datasets. |
| Approach: | They propose a framework for machine translating English multimodal data to improve training data . they propose two metrics to prevent models from learning from low-quality translated text . |
| Outcome: | The proposed framework can be applied to any multimodal dataset and model. |