We propose the Sinkhorn Knowledge Distillation (SinKD) that exploits the Sinkhorn distance to ensure a nuanced and precise assessment of the disparity between teacher and student distributions.
However, existing embedding models for text retrieval usually have three non-negligible limitations.
In order to appropriately filter multi-modality data sets on a web-scale, it becomes crucial to employ suitable filtering methods to boost performance and reduce training costs.
During the preceding biennium, vision-language pre-training has achieved noteworthy success on several downstream tasks.
Large-scale vision-language pre-training has achieved promising results on downstream tasks.
Given a database schema, Text-to-SQL aims to translate a natural language question into the corresponding SQL query.
We prove that reviving the "dead weights" by ReCU can result in a smaller quantization error.
In this paper, we show that our weight binarization provides an analytical solution by encoding high-magnitude weights into +1s, and 0s otherwise.
First, we propose an answer-aware initialization module with a gated connection layer which introduces both document and answer information to the decoder, thus helping to guide the choice of answer-focused question words.
In this paper, for the first time, we explore the influence of angular bias on the quantization error and then introduce a Rotated Binary Neural Network (RBNN), which considers the angle alignment between the full-precision weight vector and its binarized version.
In dialogue systems, a dialogue state tracker aims to accurately find a compact representation of the current dialogue status, based on the entire dialogue history.