Yiu-Chang Lin 
Email: linyiuchang AT gmail DOT com




Machine Learning Engineer at Apple (02/2020-present)


  • Siri Understanding

    • Research Scientist at RIT-Boston (08/2016-01/2020)

    Rakuten Institute of Technology, Boston


  • Query Understanding (classification & segmentation)

  • Item Product Linking

  • Product Taxonomy Classification (hierarchical classification)

  • Product Search Re-Ranking

  • Rakuten Data Challenge and SIGIR 2018 eCom Workshop

    • Research Assistant at CMU (08/2014-08/2016)

    Advisor: Prof. Eduard Hovy


  • Learning Image and Text Semantic Relatedness

    • Jointly trained two parallel deep neural networks, with 19-layer VGG image feature and word2vec sentence embeddings as inputs, respectively. The objective is to maximize the cosine similarity of the two output representations if the image/sentence pair are relevant with Mean Square Loss. It reached state-of-the-art image to sentence retrieval results on Flickr30k dataset (R@5 = 0.64, R@10 = 0.77).


  • Trilingual Entity Linking and Discovery

    • Led a team in 2105 and 2016 TAC KBP Trilingual Entity Discovery and Linking Track. Aimed to extract named entity mentions from a source collection of textual documents in multiple languages (English, Chinese and Spanish) and link them to an existing Knowledge Base (Freebase). Proposed a graph-based model that jointly tackles entity recognition and entity linking tasks together.


  • Word Sense Disambiguation via PropStore and OntoNotes

    • Constructed PropStore, a multilingual (English, Chinese and Spanish) propositional Knowledge Base of dependency relations between words from Wikipedia dump. Proposed a novel Word Sense Disambiguation algorithm that combines POS-sensitive word2vec representations and distributional information derived from PropStore and OntoNotes sense inventory.


    Research Staff at JHU HLTCOE (SCALE) (06/2015-08/2015)

    Advisor: Prof. James Mayfield and Prof. Mark Dredze


  • Chinese Entity Discovery and Linking

    • Focused on different models for Chinese entity linking, including training a Chinese to English machine translation language package from scratch using Joshua and improving Slinky, an entity linking tool that implements a highly parallel message passing infrastructure using Akka and adopts SVM learning to rank approach for entity disambiguation.


    NTU Speech Processing Lab (09/2001-06/2013)

    Advisor: Prof. Lin-Shan Lee


  • Latent Topic Modeling (Probabilistic Latent Semantic Analysis & Latent Dirichlet Allocation)

    • Developed a C++ toolkit that implements EM algorithm for Probabilistic Latent Semantic Analysis (PLSA) and Gibbs sampling for Latent Dirichlet Allocation (LDA). Applied topic models to spectrogram analysis and extracted-based text summarization task.


  • Minimum Phone Error Model Training on Merged Acoustic Units for Transcribing Bilingual Code-Switched Speech

    • Performed Minimum Phone Error (MPE) training on merged acoustic units for transcribing Chinese-English code-switched lectures with highly imbalanced language distribution. Significantly improved recognition accuracy of English from 59.9% to 68.23%.