Semantic Sentence Matching Based on Multiple Parallelly Organized Interaction Layers at Various Granularity Combinations With Two-Stage Aggregation Strategy

Semantic sentence matching plays an essential role in resolving many problems in natural language processing (NLP) field, it has gained increasing research focus and shown great improvements in recent years. However, most currently existing researches are for English sentence matching, research on Chinese semantic matching are relatively less. Moreover, due to the rather complicated contextual expressions and grammatical structure of Chinese language, many existing models are still unable to quite effectively capture interaction information between sentences. Thus, in this work, we attempt to propose a novel deep model to better address Chinese semantic sentence matching. Specifically, the convolutional neural networks with various kernel sizes are first employed for the multi-granular contextual encoding of sentences, combined with multiple different cross-sentence alignment mechanisms, the semantic interactions can be more clearly and profoundly performed at various granularity combinations between sentences. Additionally, rather than serially stacking multiple interaction layers, we organize multiple interaction layers in a parallel manner, and by further introduction of attention pooling, the semantically aligned sentence attentive vectors would be adaptively aggregated from both perspectives of alignment mechanisms and granularity combinations, thus more stable and effective sentence interactive features can be extracted while attempting to alleviate potential sentence alignment error propagation issue existed in hierarchically stacked interaction structure. Finally, extensive experiments are conducted to evaluate the performance of our model, the experimental results demonstrate that our proposed approach outperforms many state-of-the-art models on sentence matching and is capable of gaining a more accurate understanding of semantic relationships between Chinese sentences.