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Found 6 papers, 3 papers with code
On-Off Pattern Encoding and Path-Count Encoding as Deep Neural Network Representations
Specifically, we consider \textit{On-Off pattern} and \textit{PathCount} for investigating how information is stored in deep representations.
Isotropic Representation Can Improve Dense Retrieval
In particular, many of the high-performing dense retrieval models evaluate representations of query and document using BERT, and subsequently apply a cosine-similarity based scoring to determine the relevance.
Finding Inverse Document Frequency Information in BERT
The traditional approach, however, is being rapidly replaced by Neural Ranking Models (NRMs) that can exploit semantic features.
Semi-Siamese Bi-encoder Neural Ranking Model Using Lightweight Fine-Tuning
The results confirm that both lightweight fine-tuning and semi-Siamese are considerably helpful for improving BERT-based bi-encoders.
Improving Bi-encoder Document Ranking Models with Two Rankers and Multi-teacher Distillation
When monoBERT is used as the cross-encoder teacher, together with either TwinBERT or ColBERT as the bi-encoder teacher, TRMD produces a student bi-encoder that performs better than the corresponding baseline bi-encoder.
Interpreting Neural Ranking Models using Grad-CAM
However, explaining the ranking results has become even more difficult with NRM due to the complex structure of neural networks.
