论文标题
具有查询术语独立性的构象 - 内核用于文件检索
Conformer-Kernel with Query Term Independence for Document Retrieval
论文作者
论文摘要
Transformer-Kernel（TK）模型已在TREC深度学习基准上表现出强大的重新依据性能 - - 可以被认为是基于BERT的排名模型的有效（但有效）的有效替代方案。在这项工作中，我们通过合并查询术语独立假设，将TK架构扩展到完整的检索设置。此外，为了降低变压器层相对于输入序列长度的记忆复杂性，我们提出了一个新的构象异构层。我们表明，构象异构体的GPU内存需求与输入序列长度线性缩放，在排名长文档时，它是一个更可行的选项。最后，我们证明将显式项匹配信号纳入模型可能在整个检索设置中特别有用。我们在本文的工作中介绍了初步结果。
The Transformer-Kernel (TK) model has demonstrated strong reranking performance on the TREC Deep Learning benchmark---and can be considered to be an efficient (but slightly less effective) alternative to BERT-based ranking models. In this work, we extend the TK architecture to the full retrieval setting by incorporating the query term independence assumption. Furthermore, to reduce the memory complexity of the Transformer layers with respect to the input sequence length, we propose a new Conformer layer. We show that the Conformer's GPU memory requirement scales linearly with input sequence length, making it a more viable option when ranking long documents. Finally, we demonstrate that incorporating explicit term matching signal into the model can be particularly useful in the full retrieval setting. We present preliminary results from our work in this paper.
