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/* Efficient left and right language model state for sentence fragments.
* Intended usage:
* Store ChartState with every chart entry.
* To do a rule application:
* 1. Make a ChartState object for your new entry.
* 2. Construct RuleScore.
* 3. Going from left to right, call Terminal or NonTerminal.
* For terminals, just pass the vocab id.
* For non-terminals, pass that non-terminal's ChartState.
* If your decoder expects scores inclusive of subtree scores (i.e. you
* label entries with the highest-scoring path), pass the non-terminal's
* score as prob.
* If your decoder expects relative scores and will walk the chart later,
* pass prob = 0.0.
* In other words, the only effect of prob is that it gets added to the
* returned log probability.
* 4. Call Finish. It returns the log probability.
*
* There's a couple more details:
* Do not pass <s> to Terminal as it is formally not a word in the sentence,
* only context. Instead, call BeginSentence. If called, it should be the
* first call after RuleScore is constructed (since <s> is always the
* leftmost).
*
* If the leftmost RHS is a non-terminal, it's faster to call BeginNonTerminal.
*
* Hashing and sorting comparison operators are provided. All state objects
* are POD. If you intend to use memcmp on raw state objects, you must call
* ZeroRemaining first, as the value of array entries beyond length is
* otherwise undefined.
*
* Usage is of course not limited to chart decoding. Anything that generates
* sentence fragments missing left context could benefit. For example, a
* phrase-based decoder could pre-score phrases, storing ChartState with each
* phrase, even if hypotheses are generated left-to-right.
*/
#ifndef LM_LEFT_H
#define LM_LEFT_H
#include "max_order.hh"
#include "state.hh"
#include "return.hh"
#include "../util/murmur_hash.hh"
#include <algorithm>
namespace lm {
namespace ngram {
template <class M> class RuleScore {
public:
explicit RuleScore(const M &model, ChartState &out) : model_(model), out_(&out), left_done_(false), prob_(0.0) {
out.left.length = 0;
out.right.length = 0;
}
void BeginSentence() {
out_->right = model_.BeginSentenceState();
// out_->left is empty.
left_done_ = true;
}
void Terminal(WordIndex word) {
State copy(out_->right);
FullScoreReturn ret(model_.FullScore(copy, word, out_->right));
if (left_done_) { prob_ += ret.prob; return; }
if (ret.independent_left) {
prob_ += ret.prob;
left_done_ = true;
return;
}
out_->left.pointers[out_->left.length++] = ret.extend_left;
prob_ += ret.rest;
if (out_->right.length != copy.length + 1)
left_done_ = true;
}
// Faster version of NonTerminal for the case where the rule begins with a non-terminal.
void BeginNonTerminal(const ChartState &in, float prob = 0.0) {
prob_ = prob;
*out_ = in;
left_done_ = in.left.full;
}
void NonTerminal(const ChartState &in, float prob = 0.0) {
prob_ += prob;
if (!in.left.length) {
if (in.left.full) {
for (const float *i = out_->right.backoff; i < out_->right.backoff + out_->right.length; ++i) prob_ += *i;
left_done_ = true;
out_->right = in.right;
}
return;
}
if (!out_->right.length) {
out_->right = in.right;
if (left_done_) {
prob_ += model_.UnRest(in.left.pointers, in.left.pointers + in.left.length, 1);
return;
}
if (out_->left.length) {
left_done_ = true;
} else {
out_->left = in.left;
left_done_ = in.left.full;
}
return;
}
float backoffs[KENLM_MAX_ORDER - 1], backoffs2[KENLM_MAX_ORDER - 1];
float *back = backoffs, *back2 = backoffs2;
unsigned char next_use = out_->right.length;
// First word
if (ExtendLeft(in, next_use, 1, out_->right.backoff, back)) return;
// Words after the first, so extending a bigram to begin with
for (unsigned char extend_length = 2; extend_length <= in.left.length; ++extend_length) {
if (ExtendLeft(in, next_use, extend_length, back, back2)) return;
std::swap(back, back2);
}
if (in.left.full) {
for (const float *i = back; i != back + next_use; ++i) prob_ += *i;
left_done_ = true;
out_->right = in.right;
return;
}
// Right state was minimized, so it's already independent of the new words to the left.
if (in.right.length < in.left.length) {
out_->right = in.right;
return;
}
// Shift exisiting words down.
for (WordIndex *i = out_->right.words + next_use - 1; i >= out_->right.words; --i) {
*(i + in.right.length) = *i;
}
// Add words from in.right.
std::copy(in.right.words, in.right.words + in.right.length, out_->right.words);
// Assemble backoff composed on the existing state's backoff followed by the new state's backoff.
std::copy(in.right.backoff, in.right.backoff + in.right.length, out_->right.backoff);
std::copy(back, back + next_use, out_->right.backoff + in.right.length);
out_->right.length = in.right.length + next_use;
}
float Finish() {
// A N-1-gram might extend left and right but we should still set full to true because it's an N-1-gram.
out_->left.full = left_done_ || (out_->left.length == model_.Order() - 1);
return prob_;
}
void Reset() {
prob_ = 0.0;
left_done_ = false;
out_->left.length = 0;
out_->right.length = 0;
}
void Reset(ChartState &replacement) {
out_ = &replacement;
Reset();
}
private:
bool ExtendLeft(const ChartState &in, unsigned char &next_use, unsigned char extend_length, const float *back_in, float *back_out) {
ProcessRet(model_.ExtendLeft(
out_->right.words, out_->right.words + next_use, // Words to extend into
back_in, // Backoffs to use
in.left.pointers[extend_length - 1], extend_length, // Words to be extended
back_out, // Backoffs for the next score
next_use)); // Length of n-gram to use in next scoring.
if (next_use != out_->right.length) {
left_done_ = true;
if (!next_use) {
// Early exit.
out_->right = in.right;
prob_ += model_.UnRest(in.left.pointers + extend_length, in.left.pointers + in.left.length, extend_length + 1);
return true;
}
}
// Continue scoring.
return false;
}
void ProcessRet(const FullScoreReturn &ret) {
if (left_done_) {
prob_ += ret.prob;
return;
}
if (ret.independent_left) {
prob_ += ret.prob;
left_done_ = true;
return;
}
out_->left.pointers[out_->left.length++] = ret.extend_left;
prob_ += ret.rest;
}
const M &model_;
ChartState *out_;
bool left_done_;
float prob_;
};
} // namespace ngram
} // namespace lm
#endif // LM_LEFT_H
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