Papers with Open-Domain
CRAFT: Training-Free Cascaded Retrieval for Tabular QA (2026.acl-long)
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| Challenge: | Existing methods for open-domain table question answering require retraining or fine-tuning on new datasets. |
| Approach: | They propose a zero-shot, cascaded retrieval approach that uses a sparse retrieval model to filter a subset of candidates before applying more expensive dense models as re-rankers. |
| Outcome: | The proposed method outperforms state-of-the-art retrieval models on the NQ-Tables dataset. |
Phrase Retrieval for Open Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning (2023.findings-acl)
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| Challenge: | Open-Domain Conversational Question Answering (ODConvQA) aims to answer questions through a multi-turn conversation . however, such a pipeline approach makes the reader vulnerable to errors propagated from the retriever, which makes it slower since they are not runnable in parallel. |
| Approach: | They propose a method to directly predict answers with a phrase retrieval scheme for a sequence of words. |
| Outcome: | The proposed method outperforms the baselines on two ODConvQA datasets. |
Unsupervised Sign Language Translation and Generation (2024.findings-acl)
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Zhengsheng Guo, Zhiwei He, Wenxiang Jiao, Xing Wang, Rui Wang, Kehai Chen, Zhaopeng Tu, Yong Xu, Min Zhang
| Challenge: | Experimental results on the BBC-Oxford Sign Language dataset reveal that USLNet achieves competitive results compared to supervised baseline models. |
| Approach: | They propose an unsupervised sign language translation and generation network that learns from abundant single-modality data without parallel sign language data. |
| Outcome: | The proposed model achieves competitive results compared to baseline models on the BBC-Oxford Sign Language dataset and Open-Domain American Sign Language data. |