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Learning the Semantic Textual Similarity (STS) is a critical issue for many NLP tasks such as question answering, document summarization and etc.. In this paper, we combine the Multiplicative LSTM structure with a Siamese architecture which learn to project word embeddings of each sentence into a fixed-dimensional embedding space to represent this sentence. Then these sentence embeddings can be used to evaluate the STS task. We compare with several similar architectures and the proposed method has achieved better results and is competitive with the best state-of-the-art siamese neural network architecture.

Elliptical sentences are frequently seen in Chinese, especially in some particular situations, such as dialogues, which is challengeable to understand specific semantic. Chinese zero pronoun resolution, which recovers a noun phrase in the elliptical position, is an effective method to help machines understand natural languages. Traditional methods use the features, which are extracted from syntactic parsing trees manually. However, the long running time and the inaccuracy of automatic parsing algorithms have a bad influence on practical applications. In this work, we propose a new method based on long-short-term memory network that calculates dense vector representations for mention pairs without using features from syntactic parsing trees. These representations, which capture significant semantics for zero pronoun resolution, are built on distributed representation of words in surrounding contexts and candidate antecedents. Our method contributes to reducing the manual work of extracting features from parsing tress, which improves the F1-score of Chinese zero pronoun resolution system. Experimental results on OnotoNotes 5.0 Chinese dataset show our method achieves better performance compared with the state-of-the-art method.