Papers by Jheng-Hong Yang
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. |
Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math Information Retrieval (2022.findings-emnlp)
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| Challenge: | a recent study has shown that dense retrieval methods are suboptimal for capturing contextual similarities in complex data. |
| Approach: | They propose to combine a structure search method and efficient bi-encoder dense retrieval models to capture contextual similarities. |
| Outcome: | The proposed model improves on token-level and passage-level dense retrieval tasks. |