Papers by Jiali Yao

2 papers
Enabling Real-time Neural IME with Incremental Vocabulary Selection (N19-2)

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Challenge: Input method editor (IME) converts sequential alphabet key inputs to words in a target language.
Approach: They propose a neural-based language model that incrementally builds a subset vocabulary from the word lattice.
Outcome: The proposed approach achieves 50x speedup on Japanese IME benchmark without losing conversion accuracy.
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings (2021.emnlp-main)

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Challenge: Existing sentence ordering models can be classified into pairwise ordering models and set-to-sequence models.
Approach: They propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering.
Outcome: The proposed model achieves state-of-the-art performance on five commonly-used datasets.

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