BERT-DRE: BERT with Deep Recursive Encoder for Natural Language Sentence Matching
This work addresses sentence matching for NLP applications, but it is incremental as it builds on BERT with minor architectural modifications.
The paper tackled the problem of Natural Language Sentence Matching by enhancing BERT with a deep recursive encoder, resulting in improved accuracy across multiple benchmarks, including a specific gain from 89.70% to 90.29% on a Persian religious dataset.
This paper presents a deep neural architecture, for Natural Language Sentence Matching (NLSM) by adding a deep recursive encoder to BERT so called BERT with Deep Recursive Encoder (BERT-DRE). Our analysis of model behavior shows that BERT still does not capture the full complexity of text, so a deep recursive encoder is applied on top of BERT. Three Bi-LSTM layers with residual connection are used to design a recursive encoder and an attention module is used on top of this encoder. To obtain the final vector, a pooling layer consisting of average and maximum pooling is used. We experiment our model on four benchmarks, SNLI, FarsTail, MultiNLI, SciTail, and a novel Persian religious questions dataset. This paper focuses on improving the BERT results in the NLSM task. In this regard, comparisons between BERT-DRE and BERT are conducted, and it is shown that in all cases, BERT-DRE outperforms BERT. The BERT algorithm on the religious dataset achieved an accuracy of 89.70%, and BERT-DRE architectures improved to 90.29% using the same dataset.