(Almost) Zero-Shot Cross-Lingual Spoken Language Understanding

@article{Upadhyay2018AlmostZC,
  title={(Almost) Zero-Shot Cross-Lingual Spoken Language Understanding},
  author={Shyam Upadhyay and Manaal Faruqui and G. T{\"u}r and Dilek Z. Hakkani-T{\"u}r and Larry Heck},
  journal={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2018},
  pages={6034-6038}
}
  • Shyam Upadhyay, Manaal Faruqui, +2 authors Larry Heck
  • Published 2018
  • Computer Science
  • 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Spoken language understanding (SLU) is a component of goal-oriented dialogue systems that aims to interpret user's natural language queries in system's semantic representation format. While current state-of-the-art SLU approaches achieve high performance for English domains, the same is not true for other languages. Approaches in the literature for extending SLU models and grammars to new languages rely primarily on machine translation. This poses a challenge in scaling to new languages, as… CONTINUE READING
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    References

    SHOWING 1-10 OF 27 REFERENCES
    Combining multiple translation systems for Spoken Language Understanding portability
    • 19
    • Highly Influential
    • PDF
    Multi-style adaptive training for robust cross-lingual spoken language understanding
    • 23
    • PDF
    Generative and discriminative algorithms for spoken language understanding
    • 271
    • PDF
    What is left to be understood in ATIS?
    • 154
    • PDF
    Language style and domain adaptation for cross-language SLU porting
    • 14
    • PDF
    A data-driven spoken language understanding system
    • Yulan He, S. Young
    • Computer Science
    • 2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721)
    • 2003
    • 119
    • PDF
    Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding
    • 404
    • PDF
    Multi-Domain Joint Semantic Frame Parsing Using Bi-Directional RNN-LSTM
    • 253
    • PDF