10 Matching Annotations
  1. Nov 2019
    1. That's especially useful when combining it with React's slot pattern, which is used for passing multiple composed components to different places within a (render prop) component, but then advancing it with a render prop function to pass the state from the render prop component to the composed components.
  2. Mar 2019
    1. or joint modeling of intent detection and slot filling, weadd an additional decoder for intent detection (or intent clas-sification) task that shares the same encoder with slot fillingdecoder.

      本文为了对intent和slot-filling联合建模,额外添加了一个decoder来进行意图检测。

    2. The attentionmechanism later introduced in [12] enables the encoder-decodermodel to learn a soft alignment and to decode at the same time.

      本文中用到的attention-RNN算法。

      D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine trans-lation by jointly learning to align and translate,”arXiv preprintarXiv:1409.0473, 2014

    1. Dialogue State Tracking

      跟进对话状态是保障dialog system的robust的核心。主要目标是预测每轮对话的用户目标。经典的状态结构通常叫做slot-filling 或者 sematic frame.

      传统用手工规则的方法: D. Goddeau, H. Meng, J. Polifroni, S. Seneff, andS. Busayapongchai. A form-based dialogue managerfor spoken language applications. InSpoken Language,1996. ICSLP 96. Proceedings., Fourth InternationalConference on, volume 2, pages 701–704. IEEE, 1996

      基于规则的方法倾向于常见的错误,然后很多结果并不是想要的。 J. D. Williams. Web-style ranking and slu combina-tion for dialog state tracking. InSIGDIAL Conference,pages 282–291, 2014

    2. Slot filling

      填槽这个问题更多的是看成一个序列标注的问题。句子中的每个词都打上一个语义标签。输入是由词组成的句子,输出是每个词对应的slot/concept IDs.

      DBN 类的处理:

      • A Deoras and R. Sarikaya. Deep belief network basedsemantic taggers for spoken language understanding.

        L. Deng, G. Tur, X. He, and D. Hakkani-Tur. Use ofkernel deep convex networks and end-to-end learningfor spoken language understanding

      RNN:

      • G. Mesnil, X. He, L. Deng, and Y. Bengio. Investi-gation of recurrent-neural-network architectures andlearning methods for spoken language understanding.Interspeech, 2013.
      • K. Yao, G. Zweig, M. Y. Hwang, Y. Shi, and D. Yu.Recurrent neural networks for language understand-ing. InInterspeech, 2013
      • R. Sarikaya, G. E. Hinton, and B. Ramabhadran.Deep belief nets for natural language call-routing
      • K. Yao, B. Peng, Y. Zhang, D. Yu, G. Zweig, andY. Shi. Spoken language understanding using longshort-term memory neural networks. InIEEE Insti-tute of Electrical & Electronics Engineers, pages 189 –194, 2014
  3. Feb 2019
  4. www.iro.umontreal.ca www.iro.umontreal.ca
    1. For the slot filling task, the input is the sentence consisting of a sequence of words, L, and the output is a sequence of slot/concept IDs, S, one for each word. In the statistical SLU systems, the task is often formalized as a pattern recognition problem: Given the word sequence L, the goal of SLU is to find the semantic representation of the slot sequence 푆that has the maximum a posterioriprobability 푃(푆|퐿).

      对于填槽任务,输入是一个有一系列词组成的语句,输出是每个词对应的slot/concept IDs。在统计SLU系统里,这个任务可以看作是:给定词序列L,SLU的目标是找到一个slot 序列来最大化后验概率P(S/L).

    2. bi-directional Jordan-type network that takes into account both past and future dependencies among slots works best

      双向的 Jordan-type网络对槽最好用

  5. Mar 2018