Papers with text model
Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects (2026.acl-long)
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| Challenge: | Research on cross-dialectal transfer from a standard to a non-standard dialect variety has typically focused on text data. |
| Approach: | They compare standard-to-dialect transfer in three settings: text models, speech models, and cascaded systems where speech first gets automatically transcribed and then further processed by a text model. |
| Outcome: | The proposed model performs best on German dialect data while the text-only model perform best on the standard data. |
Using Social and Linguistic Information to Adapt Pretrained Representations for Political Perspective Identification (2021.findings-acl)
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| Challenge: | a new framework for political perspective detection is proposed to improve text training costs . current deep learning models lack the ability to focus on text span for bias detection . |
| Approach: | They propose a framework that pretrains the text model using social and linguistic contexts . they demonstrate that the framework improves performance by identifying bias-related text spans based on entity mentions and news sharing . |
| Outcome: | The proposed framework improves on two news bias datasets and improves performance on the general source and task. |
LAMBDA: Large Language Model-Based Data Augmentation for Multi-Modal Machine Translation (2024.findings-emnlp)
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| Challenge: | Multi-modal machine translation methods are underperforming compared to pre-trained models due to lack of triplet training data. |
| Approach: | They propose a multi-modal machine translation method that integrates images and visual modality to enhance language understanding. |
| Outcome: | The proposed method can enrich the original samples and expand the dataset without requiring external images and text. |
(Almost) Free Modality Stitching of Foundation Models (2025.emnlp-main)
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| Challenge: | Multi-modal foundation models often use modality-specific (uni-modal) models as sub-components, which are stitched together via a connector module. |
| Approach: | They propose a framework that allows for optimal uni-modal model selection and connector training by leveraging hypernetworks. |
| Outcome: | The proposed framework reduces the cost of searching for the best performing uni-modal model pair by 10 while matching the ranking and trained connector performance across diverse multi-modal benchmarks. |