Papers by Chengbo Jiao

2 papers
Bridging the Granularity Gap for Acoustic Modeling (2023.findings-acl)

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Challenge: Despite the success of speech recognition, how to encode the speech features effectively remains an open problem.
Approach: They propose a Progressive Down-Sampling technique which compresses acoustic features into coarser-grained units containing more complete semantic information, like text-level representation.
Outcome: The proposed method yields comparable or better results on the speech recognition task and inference speedups ranging from 1.20x to 1.47x.
Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest (2022.coling-1)

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Challenge: Medical relation extraction (MRE) tasks aims to extract relations between entities in medical literature.
Approach: They propose to combine semantic and syntactic information from medical texts by using causal explanation theory.
Outcome: Empirically, the proposed model outperforms existing methods on benchmark medical datasets.

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