Name
Title | ||
|---|---|---|
Sentence Pair Similarity Modeling Based On Weighted Interaction Of Multi-Semantic Embedding Matrix |
Abstract | ||
|---|---|---|
In this paper, we focus on measuring the similarity of sentence pair. Noting that a single sentence vector may lose fine-grained semantic information which is important for sentence matching, we propose an embedding matrix to calculate a multi-granularity similarity matrix and find the true semantic alignment of two sentences. We also propose a semantic importance calculation and semantic decomposition that are simple but effective. The proposed model does not require any sparse features or external resources such as WordNet. Compared with other state-of-the-art models, we successfully train in a short time and achieve competitive results on similarity measurement and paraphrase identification tasks. Experiments and visual analysis show the good performance and interpretability of the model. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICTAI50040.2020.00170 | 2020 IEEE 32ND INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI) |
Keywords | DocType | ISSN |
semantic embedding matrix, sentence similarity, sentence alignment, weighted interaction | Conference | 1082-3409 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junyu Chen | 1 | 19 | 6.56 |
| Xiaohong Zhu | 2 | 0 | 0.34 |
| Jun Sang | 3 | 40 | 12.62 |
| Lu Gong | 4 | 0 | 0.34 |