Papers with transfer learning strategies
XL-NBT: A Cross-lingual Neural Belief Tracking Framework (D18-1)
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| Challenge: | a multi-lingual approach to training dialog systems is expensive and tedious, but it can be useful for cross-lingual support. |
| Approach: | They propose to annotate data for multiple languages and train a multi-lingual dialog system for each language. |
| Outcome: | The proposed framework bypasses the expensive human annotation and achieves promising results. |
Exploring Neural Models for Query-Focused Summarization (2022.findings-naacl)
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| Challenge: | Recent work in Query-focused summarization lacks a comprehensive study of the broad space of applicable modeling methods. |
| Approach: | They propose to explore two general classes of methods for Query-focused summarization: extractive-abstractive solutions and end-to-end models. |
| Outcome: | The proposed models achieve state-of-the-art on the QMSum dataset, with a margin of 3.38 ROUGE-1, 3.72 ROUGe2 and 3.28 ROUGEL-L. |
Multi-Aspect Transfer Learning for Detecting Low Resource Mental Disorders on Social Media (2022.lrec-1)
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| Challenge: | Mental disorders are an important and pervasive public health issue. |
| Approach: | They propose to use linguistic features to improve mental disorder detection . they propose to apply multi-aspect transfer learning to detecting disorders from social media . |
| Outcome: | The proposed methods can be used to improve mental disorder detection in the context of data scarcity and understanding the overlapping symptoms between disorders. |
MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning (2023.acl-long)
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| Challenge: | Experimental results show zero-shot performance on unseen multimodal tasks . instruction tuning has yet to be explored for vision and multimodal task. |
| Approach: | They propose a multimodal instruction tuning benchmark dataset that consists of 62 diverse multimodal tasks in a unified seq-to-seq format covering 10 broad categories. |
| Outcome: | The proposed model performs well on unseen multimodal tasks and is highly scalable. |