dmlc--dgl
Minjie Wang
17 行
882 B
Plaintext
17 行
882 B
Plaintext
.. _tutorials2-index:
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Dealing with many small graphs
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==============================
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* **Tree-LSTM** `[paper] <https://arxiv.org/abs/1503.00075>`__ `[tutorial]
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<2_small_graph/3_tree-lstm.html>`__ `[code]
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<https://github.com/dmlc/dgl/blob/master/examples/pytorch/tree_lstm>`__:
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sentences of natural languages have inherent structures, which are thrown
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away by treating them simply as sequences. Tree-LSTM is a powerful model
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that learns the representation by leveraging prior syntactic structures
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(e.g. parse-tree). The challenge to train it well is that simply by padding
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a sentence to the maximum length no longer works, since trees of different
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sentences have different sizes and topologies. DGL solves this problem by
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throwing the trees into a bigger "container" graph, and use message-passing
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to explore maximum parallelism. The key API we use is batching.
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