<p><a href="https://github.com/FredericGodin/DynamicCNN" rel="nofollow noreferrer">DynamicCNN - for Theano/Lasagne </a> by <a href="http://www.fredericgodin.com/" rel="nofollow noreferrer">Fréderic Godin</a> is an approach which might work better for sentences modeling. It is based on a paper named <a href="https://arxiv.org/abs/1404.2188" rel="nofollow noreferrer">"A Convolutional Neural Network for Modelling Sentences"</a> by <a href="https://www.nal.ai/" rel="nofollow noreferrer">Nal Kalchbrenner</a>, Edward Grefenstette, Phil Blunsom from 2014.</p>

<p>Quoting the abstract of the mentioned paper:</p>

<blockquote>
  <p>The network uses Dynamic k-Max Pooling, a global pooling operation
  over linear sequences. The network handles input sentences of varying
  length and induces a feature graph over the sentence that is capable
  of explicitly capturing short and long-range relations. The network
  does not rely on a parse tree and is easily applicable to any
  language. We test the DCNN in four experiments: small scale binary and
  multi-class sentiment prediction, six-way question classification and
  Twitter sentiment prediction by distant supervision. The network
  achieves excellent performance in the first three tasks and a greater
  than 25% error reduction in the last task with respect to the
  strongest baseline.</p>
</blockquote>

<p>I hadn't used it myself, but it solved another  similar <a href="https://stackoverflow.com/questions/38628014/variable-size-convolutional-neural-network-input-and-fixed-output">sentences modeling problem</a> on SO.</p>
How to use CNN to train input data of different size?
<p>CNN seems to be implemented mostly for fixed size input. Now I want to use CNN to train some sentences of different size, what are some common methods?</p>