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authorPatrick Simianer <p@simianer.de>2013-11-13 18:28:42 +0100
committerPatrick Simianer <p@simianer.de>2013-11-13 18:28:42 +0100
commit4c7d24c9357f500839f04c7c8a8cfa0472801e18 (patch)
tree19fceda02d09c9d4990aba692ed97193020d1e86
parenta5909686af9fef41ca05abde519a06c5b5382225 (diff)
README
-rw-r--r--training/dtrain/README.md30
1 files changed, 12 insertions, 18 deletions
diff --git a/training/dtrain/README.md b/training/dtrain/README.md
index 2bae6b48..aa1ab3e7 100644
--- a/training/dtrain/README.md
+++ b/training/dtrain/README.md
@@ -1,10 +1,15 @@
This is a simple (and parallelizable) tuning method for cdec
-which is able to train the weights of very many (sparse) features.
-It was used here:
- "Joint Feature Selection in Distributed Stochastic
- Learning for Large-Scale Discriminative Training in
- SMT"
-(Simianer, Riezler, Dyer; ACL 2012)
+which is able to train the weights of very many (sparse) features
+on the training set.
+
+It was used in these papers:
+> "Joint Feature Selection in Distributed Stochastic
+> Learning for Large-Scale Discriminative Training in
+> SMT" (Simianer, Riezler, Dyer; ACL 2012)
+>
+> "Multi-Task Learning for Improved Discriminative
+> Training in SMT" (Simianer, Riezler; WMT 2013)
+>
Building
@@ -17,20 +22,9 @@ To build only parts needed for dtrain do
cd training/dtrain/; make
```
-Ideas
------
- * get approx_bleu to work?
- * implement minibatches (Minibatch and Parallelization for Online Large Margin Structured Learning)
- * learning rate 1/T?
- * use an oracle? mira-like (model vs. BLEU), feature repr. of reference!?
- * implement lc_bleu properly
- * merge kbest lists of previous epochs (as MERT does)
- * ``walk entire regularization path''
- * rerank after each update?
-
Running
-------
-See directories under test/ .
+See directories under examples/ .
Legal
-----