Morphological Analysis and Generation › Key Parameters
Michael BrenndoerferMay 3, 20251 min read
Language AI HandbookAll chapters Part 7 of 67Linguistic Form: Morphology and Syntax
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The key parameters for the neural seq2seq inflector are:
- emb_dim = 32: Dimension of the character embeddings.
- hid_dim = 64: Hidden-state size of both GRUs. The decoder's attention can access the full sequence of encoder states, so this is not a single-vector stem bottleneck.
- tf_ratio = 1.0: Probability of teacher forcing at each training decoder step in this experiment. Lower values expose the decoder to its own earlier predictions, but do not by themselves guarantee better copying.
- lr = 3e-3: Learning rate for the Adam optimizer used for the 60-epoch run.
In the next chapter we climb one level of linguistic structure, from the inside of words to the arrangement of words into phrases, with grammars and constituency structure. When we reach dependency syntax and Universal Dependencies later in this part, you will see these morphological feature bundles attached to the tokens for which the relevant features are annotated; a token's FEATS field may also be empty.