4 citations · 15 across the 10 of their papers we have counts for
10 papers
HypR: A comprehensive study for ASR hypothesis revising with a reference corpus
Yi-Wei Wang, Ke-Han Lu, Kuan-Yu Chen
With the development of deep learning, automatic speech recognition (ASR) has made significant progress. To further enhance the performance of ASR, revising recognition results is…
DDNAS: Discretized Differentiable Neural Architecture Search for Text Classification
Kuan-Chun Chen, Cheng-Te Li, Kuo-Jung Lee
Neural Architecture Search (NAS) has shown promising capability in learning text representation. However, existing text-based NAS neither performs a learnable fusion of neural oper…
Trompt: Towards a Better Deep Neural Network for Tabular Data
Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou +2
Tabular data is arguably one of the most commonly used data structures in various practical domains, including finance, healthcare and e-commerce. The inherent heterogeneity allows…
A Lexical-aware Non-autoregressive Transformer-based ASR Model
Chong-En Lin, Kuan-Yu Chen
Non-autoregressive automatic speech recognition (ASR) has become a mainstream of ASR modeling because of its fast decoding speed and satisfactory result. To further boost the perfo…
Towards Improving the Explainability of Text-based Information Retrieval with Knowledge Graphs
Boqi Chen, Kua Chen, Yujing Yang +6
Thanks to recent advancements in machine learning, vector-based methods have been adopted in many modern information retrieval (IR) systems. While showing promising retrieval perfo…
A context-aware knowledge transferring strategy for CTC-based ASR
Ke-Han Lu, Kuan-Yu Chen
Non-autoregressive automatic speech recognition (ASR) modeling has received increasing attention recently because of its fast decoding speed and superior performance. Among represe…
Learning to Distill: The Essence Vector Modeling Framework
Kuan-Yu Chen, Shih-Hung Liu, Berlin Chen +1
In the context of natural language processing, representation learning has emerged as a newly active research subject because of its excellent performance in many applications. Lea…
Novel Word Embedding and Translation-based Language Modeling for Extractive Speech Summarization
Kuan-Yu Chen, Shih-Hung Liu, Berlin Chen +2
Word embedding methods revolve around learning continuous distributed vector representations of words with neural networks, which can capture semantic and/or syntactic cues, and in…
Improved Spoken Document Summarization with Coverage Modeling Techniques
Kuan-Yu Chen, Shih-Hung Liu, Berlin Chen +1
Extractive summarization aims at selecting a set of indicative sentences from a source document as a summary that can express the major theme of the document. A general consensus o…
Leveraging Word Embeddings for Spoken Document Summarization
Kuan-Yu Chen, Shih-Hung Liu, Hsin-Min Wang +2
Owing to the rapidly growing multimedia content available on the Internet, extractive spoken document summarization, with the purpose of automatically selecting a set of representa…