Papers by Sheng-Chieh Lin
mAggretriever: A Simple yet Effective Approach to Zero-Shot Multilingual Dense Retrieval (2023.emnlp-main)
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| Challenge: | MLIR requires human annotations in multiple languages, making training labor-intensive. |
| Approach: | They propose a multilingual information retrieval model that leverages pre-trained multilingual transformers for dense retrieval. |
| Outcome: | Empirical results show that mAggretriever outperforms state-of-the-art models fine-tuned on English training data. |
CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrieval (2023.acl-long)
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Minghan Li, Sheng-Chieh Lin, Barlas Oguz, Asish Ghoshal, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, Xilun Chen
| Challenge: | Existing multi-vector retrieval methods are slower and require more space to store indices compared to their single-vektor counterparts. |
| Approach: | They propose a multi-vector retrieval method that uses dynamic lexical routing to route different token vectors to the predicted lexicals. |
| Outcome: | The proposed method achieves state-of-the-art performance on several benchmark datasets while being nearly 40 times faster than the current state-out-of the-art method. |
Aggretriever: A Simple Approach to Aggregate Textual Representations for Robust Dense Passage Retrieval (2023.tacl-1)
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| Challenge: | Pre-trained language models have been successful in knowledge-intensive tasks, but recent research calls into question the robustness of these singlevector models. |
| Approach: | They propose to exploit knowledge in a pre-trained language model for dense passage retrieval by aggregating contextualized token embeddings into a dense vector. |
| Outcome: | The proposed model significantly improves the effectiveness of dense retrieval models on in-domain and zero-shot evaluations without introducing substantial training overhead. |
Unifying Multimodal Retrieval via Document Screenshot Embedding (2024.emnlp-main)
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| Challenge: | Existing document retrieval pipelines require document parsing and content extraction to prepare input for indexing. |
| Approach: | They propose a retrieval paradigm that regards document screenshots as a unified input format . they leverage a large vision-language model to directly encode document screenshot into dense representations . |
| Outcome: | The proposed method outperforms existing retrieval pipelines in a text-intensive context. |
Designing Templates for Eliciting Commonsense Knowledge from Pretrained Sequence-to-Sequence Models (2020.coling-main)
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| Challenge: | Existing approaches to extract implicit knowledge from pretrained models are still unclear. |
| Approach: | They propose to use a template-based approach to extract implicit knowledge for commonsense reasoning on multiple-choice questions. |
| Outcome: | The proposed template can be extended to other MC tasks with contexts such as supporting facts in open-book question answering settings. |
Contextualized Query Embeddings for Conversational Search (2021.emnlp-main)
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| Challenge: | Existing approaches to conversational search use multiple inference pipelines that require long inference times . despite their effectiveness, such a pipeline often includes multiple neural models that require longer inference time. |
| Approach: | They propose to integrate conversational query reformulation directly into a dense retrieval model . they use a dataset with pseudo-relevance labels to overcome the lack of training data . |
| Outcome: | The proposed model rewrites conversational queries as dense representations in conversational search and open-domain question answering datasets. |
How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval (2023.findings-emnlp)
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Sheng-Chieh Lin, Akari Asai, Minghan Li, Barlas Oguz, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, Xilun Chen
| Challenge: | Existing techniques to improve dense retrieval suffer from effectiveness tradeoffs between supervised and zero-shot retrieval, some argue due to the limited model capacity. |
| Approach: | They propose to use diverse queries and sources of supervision to train a generalizable DR to achieve high accuracy in both supervised and zero-shot retrieval. |
| Outcome: | The proposed DR can achieve state-of-the-art in supervised and zero-shot evaluations without increasing model size. |