<h2>seq2seq Models</h2>
<p>A sequence to sequence (or seq2seq) model is neural architecture used for translation (and other tasks) which consists of an encoder and a decoder.</p>
<p>The encoder/decoder architecture has obvious promise for machine translation, and has been successfully applied this way.  Encoding an input to a small number of hidden nodes which can effectively be decoded to a matching string requires machine learning to learn an efficient representation of the essence of the strings.</p>
<p>In addition to translation, seq2seq models have been used in a number of other NLP tasks such as summarization and image captioning.</p>
<h3>Related links</h3>
<ul>
<li>
<p><a href="https://google.github.io/seq2seq/">tf-seq2seq</a></p>
</li>
<li>
<p><a href="https://arxiv.org/abs/1507.01053v1">Describing Multimedia Content using Attention-based Encoder--Decoder Networks</a></p>
</li>
<li>
<p><a href="https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Vinyals_Show_and_Tell_2015_CVPR_paper.pdf">Show and Tell: A Neural Image Caption Generator</a></p>
</li>
<li>
<p><a href="https://arxiv.org/pdf/1704.06485.pdf">Attend to You: Personalized Image Captioning
with Context Sequence Memory Networks</a></p>
</li>
</ul>