Siamese Recurrent Architectures for Learning Sentence Similarity

  title={Siamese Recurrent Architectures for Learning Sentence Similarity},
  author={Jonas Mueller and Aditya Thyagarajan},
We present a siamese adaptation of the Long Short-Term Memory (LSTM) network for labeled data comprised of pairs of variable-length sequences. Our model is applied to assess semantic similarity between sentences, where we exceed state of the art, outperforming carefully handcrafted features and recently proposed neural network systems of greater complexity. For these applications, we provide wordembedding vectors supplemented with synonymic information to the LSTMs, which use a fixed size… CONTINUE READING
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Skip-Thought Vectors

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On the Computational Power of Neural Nets

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