Papers by Elaine Zosa
Multilingual and Multimodal Topic Modelling with Pretrained Embeddings (2022.coling-1)
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| Challenge: | a novel neural topic model for comparable data maps texts from multiple languages and images into a shared topic space. |
| Approach: | They propose a novel multimodal multilingual neural topic model that maps texts from multiple languages and images into a shared topic space. |
| Outcome: | The proposed model outperforms a zero-shot topic model in predicting topic distributions for comparable multilingual data and performs as well on unaligned embeddings as it does on aligned embeds. |
Grounded and well-rounded: a methodological approach to the study of cross-modal and cross-lingual grounding (2023.findings-emnlp)
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| Challenge: | Existing studies on grounding have focused on qualitatively different generalizations, but limited empirical evidence supports either position. |
| Approach: | They propose a methodological framework for studying the effects of grounding on NLP systems . they use a sample of models trained on different input modalities to tease out qualitative differences . |
| Outcome: | The proposed framework teases out qualitative differences in model behavior between models trained on different input sources from quantifiable models. |
Effectiveness of Data Augmentation and Pretraining for Improving Neural Headline Generation in Low-Resource Settings (2022.lrec-1)
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| Challenge: | Neural approaches for natural language generation (NLG) have mushroomed due to large textual resources. |
| Approach: | They propose to use a pretrained multilingual encoder-decoder model and a combination of two pretrained language models to train a model in a low-resource setting. |
| Outcome: | The proposed model outperforms the previous model on English and on a small subset of the same data. |