Existing neural models are demonstrated to struggle with compositional generalization (CG), i.e., the ability to systematically generalize to unseen compositions of seen components. A key reason for failure on CG is that the syntactic and semantic representations of sequences in both the uppermost layer of the encoder and decoder are entangled. However, previous work concentrates on separating the learning of syntax and semantics instead of exploring the reasons behind the representation entanglement (RE) problem to solve it. We explain why it exists by analyzing the representation evolving mechanism from the bottom to the top of the Transformer layers. We find that the ``shallow'' residual connections within each layer fail to fuse previous layers' information effectively, leading to information forgetting between layers and further the RE problems. Inspired by this, we propose LRF, a novel Layer-wise Representation Fusion framework for CG, which learns to fuse previous layers' information back into the encoding and decoding process effectively through introducing a fuse-attention module at each encoder and decoder layer. LRF achieves promising results on two realistic benchmarks, empirically demonstrating the effectiveness of our proposal. Codes are available at https://github.com/thinkaboutzero/LRF.

Significant research has been dedicated to active probing methods used to estimate the end-to-end available bandwidth (avail-bw) of a network path. The measurement speed of an avail-bw estimation tool is vital to real-time network services such as transport control protocol (TCP) slow start, path selection for P2P applications and video streaming services and so on. In addition, a fast avail-bw estimation tool sends less probing traffic to a measured network. Pathload, a one way delay (OWD) based tool, has been widely regarded as one of the most accurate tools, but its estimation speed is often 10 or more seconds per measurement. A fast OWD-based avail-bw estimation scheme has been explored. This scheme is based on the proposed asymptotic OWD comparison model, and an estimation tool called Pathpair has been developed. Validated in a real Internet environment, experimental results show that with Pathpair the measurement speed has been significantly improved (1–3 s per measurement), while preserving accuracy compared with other major tools like Pathload and Spruce.