Name
Abstract | ||
|---|---|---|
Sentence semantic matching (SSM) is a fundamental research task in natural language processing. Most existing SSM methods take the advantage of sentence representation learning to generate a single or multi-granularity semantic representation for sentence matching. However, sentence interactions and loss function which are the two key factors for SSM still have not been fully considered. Accordingly, we propose a deep neural network architecture for SSM task with a sentence interactive matching layer and an optimised loss function. Given two input sentences, our model first encodes them to embeddings with an ordinary long short-term memory (LSTM) encoder. Then, the encoded embeddings are handled by an attention layer to find the key and important words in the sentences. Next, sentence interactions are captured with a matching layer to output a matching vector. Finally, based on the matching vector, a fully connected multi-layer perceptron outputs the similarity score. The model also distinguishes the equivocation training instances with an improved optimised loss function. We also systematically evaluate our model on a public Chinese semantic matching corpus, BQ corpus. The results demonstrate that our model outperforms the state-of-the-art methods, i.e., BiMPM, DIIN. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1504/IJCSE.2020.106870 | INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING |
Keywords | DocType | Volume |
sentence matching, representation learning, sentence interaction, loss function, deep neural model, long short-term memory, LSTM | Journal | 21 |
Issue | ISSN | Citations |
4 | 1742-7185 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xu Zhang | 1 | 7 | 2.86 |
| Wenpeng Lu | 2 | 0 | 0.34 |
| Fangfang Li | 3 | 6 | 1.49 |
| Ruoyu Zhang | 4 | 1 | 0.69 |
| Jinyong Cheng | 5 | 0 | 0.34 |