Sentence Similarity Learning by Lexical Decomposition and Composition
Table 2: Results on the QASent dataset.
['Models', 'MAP', 'MRR']
QASent dataset. Given a pair of sentences, \newciteseveryn2015learning employed a CNN model to compose each sentence into a vector separately, and joined the two sentence vectors to compute the sentence similarity. Therefore, the lower-level granularity is an indispensable factor for a good performance. \newcitewang2015faq conducted word alignment for a sentence pair based on word vectors, and measured the sentence similarity based on a couple of word alignment features. \newcitecicero2016attentive introduced the attention mechanism into the CNN model, and learnt sentence representation by considering the influence of the other sentence. They got better performance than all the other previous work. Our model makes use of all these useful factors and also considers the dissimilarities of a sentence pair. WikiQA dataset. \newciteyang2015wikiqa constructed the dataset and reimplemented several baseline models. \newcitemiao2015neural models the sentence similarity by enriching LSTMs with a latent stochastic attention mechanism. The semantic matching phase in our model is similar to the attention mechanism. But different from the previous models, our model utilizes both the similarity and dissimilarity simultaneously.