diff options
Diffstat (limited to 'decoder/ff_lm.cc')
| -rw-r--r-- | decoder/ff_lm.cc | 18 |
1 files changed, 11 insertions, 7 deletions
diff --git a/decoder/ff_lm.cc b/decoder/ff_lm.cc index 7e3f6e2b..cdfbb96a 100644 --- a/decoder/ff_lm.cc +++ b/decoder/ff_lm.cc @@ -2,7 +2,7 @@ //TODO: allow features to reorder by heuristic*weight the rules' terminal phrases (or of hyperedges'). if first pass has pruning, then compute over whole ruleset as part of heuristic -//TODO: verify that this is true: if ngram order is bigger than lm state's, then the longest possible ngram scores are still used. if you really want a lower order, a truncated copy of the LM should be small enough. otherwise, an option to null out words outside of the order's window would need to be implemented. +//NOTE: if ngram order is bigger than lm state's, then the longest possible ngram scores are still used. if you really want a lower order, a truncated copy of the LM should be small enough. otherwise, an option to null out words outside of the order's window would need to be implemented. #include "ff_lm.h" @@ -160,13 +160,14 @@ struct LMClient { class LanguageModelImpl { public: explicit LanguageModelImpl(int order) : - ngram_(*TD::dict_, order), buffer_(), order_(order), state_size_(OrderToStateSize(order) - 1), + ngram_(*TD::dict_, order), buffer_(), order_(order), state_size_(OrderToStateSize(order) - 1), floor_(-100.0), kSTART(TD::Convert("<s>")), kSTOP(TD::Convert("</s>")), kUNKNOWN(TD::Convert("<unk>")), kNONE(-1), - kSTAR(TD::Convert("<{STAR}>")) {} + kSTAR(TD::Convert("<{STAR}>")) + , unigram(order<=1) {} LanguageModelImpl(int order, const string& f) : ngram_(*TD::dict_, order), buffer_(), order_(order), state_size_(OrderToStateSize(order) - 1), @@ -175,7 +176,9 @@ class LanguageModelImpl { kSTOP(TD::Convert("</s>")), kUNKNOWN(TD::Convert("<unk>")), kNONE(-1), - kSTAR(TD::Convert("<{STAR}>")) { + kSTAR(TD::Convert("<{STAR}>")) + , unigram(order<=1) + { File file(f.c_str(), "r", 0); assert(file); cerr << "Reading " << order_ << "-gram LM from " << f << endl; @@ -264,7 +267,7 @@ class LanguageModelImpl { //TODO: make sure that Vocab_None is set to kNONE in srilm (-1), or that SRILM otherwise interprets -1 as a terminator and not a word double EstimateProb(const void* state) { - if (!order_) return 0.; + if (unigram) return 0.; int len = StateSize(state); // cerr << "residual len: " << len << endl; buffer_.resize(len + 1); @@ -278,7 +281,7 @@ class LanguageModelImpl { // for <s> (n-1 left words) and (n-1 right words) </s> double FinalTraversalCost(const void* state) { - if (!order_) return 0.; + if (unigram) return 0.; int slen = StateSize(state); int len = slen + 2; // cerr << "residual len: " << len << endl; @@ -328,7 +331,7 @@ class LanguageModelImpl { //NOTE: this is where the scoring of words happens (heuristic happens in EstimateProb) double LookupWords(const TRule& rule, const vector<const void*>& ant_states, void* vstate) { - if (order_==0) + if (unigram) return stateless_cost(rule); int len = rule.ELength() - rule.Arity(); for (int i = 0; i < ant_states.size(); ++i) @@ -399,6 +402,7 @@ public: const WordID kUNKNOWN; const WordID kNONE; const WordID kSTAR; + const bool unigram; }; struct ClientLMI : public LanguageModelImpl |