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-(graehl, comments on code)
-
-passive chart: completion of actual translation rules (X or S NT in Hiero), have
-rule features.  Hyperedge inserted with copy of rule feature vector
-(non-sparse).  Inefficient; should be postponed on intermediate parses with
-global pruning; just keep pointer to rules and models must provide an interface
-to build a (sparse) feat. vector on demand later for the stuff we keep.
-
-multithreading: none.  list of hyperarcs for refinement would need to be
-segregated into subforest blocks and have own output lists for later merging.
-e.g. bottom up count number of tail-reachable nodes under each hypernode, then
-assign to workers.
-
-ngram caching: trie, no locks, for example.  for threading, LRU hashing w/ locks per bucket is probably better, or per-thread caches.  probably cache is reset per sentence?
-
-randlm worth using?  guess not.
-
-actually get all 0-state models in 1st pass parse and prune passive edges per span.
-
-allocate cube pruning budget per prev pass
-
-(has been tried in ISI decoder) models with nonstandard state comparison,
-typically (partially) greedy forest scoring, some part of the state is excluded
-from equality/hashing.  Within virtual ff interface, would just add equals, hash
-to vtable (rather than the faster raw state compare).  If this is done often,
-then add a nonvirtual flag to interface instead, saying whether to use the
-virt. methods or not.  or: simple flag by user of ApplyModels (per feature?)
-saying whether to be 100% greedy or 0% - no halfway e.g. item name uses bigram
-context, but score using 5gram state.