Knowledge Distillation in Document Retrieval

By Siamak Shakeri, Abhinav Sethy, Cheng Cheng. 2019

7 citations recorded by the source corpus.

Complex deep learning models now achieve state of the art performance for many document retrieval tasks. The best models process the query or claim jointly with the document. However for fast scalable search it is desirable to have document embeddings which are independent of the claim. In this paper we show that knowledge distillation can be used to encourage a model that generates claim independent document encodings to mimic the behavior of a more complex model which generates claim dependent encodings. We explore this approach in document retrieval for a fact extraction and verification task. We show that by using the soft labels from a complex cross attention teacher model, the performance of claim independent student LSTM or CNN models is improved across all the ranking metrics. The student models we use are 12x faster in runtime and 20x smaller in number of parameters than the teacher

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Knowledge Distillation: A Survey (2020, methodology): “In particular, these KD-based speech recognition applications have spoken language identiﬁcation (Shen et al. 2018, 2019c, 2020), audio classiﬁcation (Gao et al. 2019; Perez et al. 2020), text-independent speaker recognition (Ng et al. 2018), speech enhancement (Watanabe et al. 2017), acoustic…”