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Only compute approximate bleu score on the hypothesis expansion.

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git-svn-id: https://mosesdecoder.svn.sourceforge.net/svnroot/mosesdecoder/branches/mira-mtm5@3478 1f5c12ca-751b-0410-a591-d2e778427230
This commit is contained in:
oliver-wilson 2010-09-16 12:38:00 +00:00
parent aece4437c2
commit 7136dcbf6d
2 changed files with 31 additions and 16 deletions
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41
moses/src/BleuScoreFeature.cpp
View File

@ -19,14 +19,19 @@ int BleuScoreState::Compare(const FFState& o) const
const BleuScoreState& other = dynamic_cast<const BleuScoreState&>(o);
if (m_target_length < other.m_target_length)
if (m_num_new_words < other.m_num_new_words)
return -1;
if (m_target_length > other.m_target_length)
if (m_num_new_words > other.m_num_new_words)
return 1;
if (m_ref_length < other.m_ref_length)
if (m_source_length < other.m_source_length)
return -1;
if (m_ref_length > other.m_ref_length)
if (m_source_length > other.m_source_length)
return 1;
if (m_scaled_ref_length < other.m_scaled_ref_length)
return -1;
if (m_scaled_ref_length > other.m_scaled_ref_length)
return 1;
int c = m_words.Compare(other.m_words);
@ -54,13 +59,13 @@ BleuScoreFeature::BleuScoreFeature() {
}
void BleuScoreFeature::LoadReferences(const std::vector<
std::vector< std::string > > &refs)
void BleuScoreFeature::LoadReferences(std::vector<
std::vector< std::string > >refs)
{
FactorCollection& fc = FactorCollection::Instance();
for (size_t ref_id = 0; ref_id < refs.size(); ref_id++) {
const std::vector< std::string >& ref = refs[ref_id];
std::pair< size_t, NGrams > ref_pair;
std::vector< std::string >& ref = refs[ref_id];
std::pair< size_t, std::map< Phrase, size_t > > ref_pair;
ref_pair.first = ref.size();
for (size_t order = 1; order < BleuScoreState::bleu_order; order++) {
for (size_t end_idx = order; end_idx < ref.size(); end_idx++) {
@ -87,7 +92,7 @@ FFState* BleuScoreFeature::Evaluate(const Hypothesis& cur_hypo,
const FFState* prev_state,
ScoreComponentCollection* accumulator) const
{
NGrams::const_iterator reference_ngrams_iter;
std::map< Phrase, size_t >::const_iterator reference_ngrams_iter;
const BleuScoreState& ps = dynamic_cast<const BleuScoreState&>(*prev_state);
BleuScoreState* new_state = new BleuScoreState(ps);
@ -106,7 +111,13 @@ FFState* BleuScoreFeature::Evaluate(const Hypothesis& cur_hypo,
for (size_t i = 0; i < num_new_words; i++) {
for (size_t n = 0; n < BleuScoreState::bleu_order; n++) {
ngram_end_idx = new_words.GetSize() - i;
// Break if we don't have enough context to do ngrams of order n.
if (ngram_end_idx < n)
break;
ngram_start_idx = ngram_end_idx - n;
Phrase ngram = new_words.GetSubString(WordsRange(ngram_start_idx,
ngram_end_idx));
@ -125,9 +136,10 @@ FFState* BleuScoreFeature::Evaluate(const Hypothesis& cur_hypo,
ctx_start_idx = ctx_end_idx - (BleuScoreState::bleu_order - 1);
new_state->m_words = new_words.GetSubString(WordsRange(ctx_start_idx,
ctx_start_idx));
new_state->m_target_length = cur_hypo.GetSize();
new_state->m_ref_length = cur_hypo.GetWordsBitmap().GetNumWordsCovered() /
m_cur_ref_length;
new_state->m_num_new_words = new_words.GetSize();
new_state->m_source_length = cur_hypo.GetSourcePhrase()->GetSize();
new_state->m_scaled_ref_length = m_cur_ref_length * (
new_state->m_num_new_words / new_state->m_source_length);
// Calculate new bleu.
new_bleu = CalculateBleu(new_state);
@ -146,8 +158,9 @@ float BleuScoreFeature::CalculateBleu(BleuScoreState* state) const {
precision *= state->m_ngram_matches[i] / state->m_ngram_counts[i];
// Apply brevity penalty if applicable.
if (state->m_target_length < state->m_ref_length)
precision *= exp(1 - state->m_ref_length / state->m_target_length);
if (state->m_num_new_words < state->m_scaled_ref_length)
precision *= exp(1 - state->m_scaled_ref_length /
state->m_num_new_words);
return precision;
}

6
moses/src/BleuScoreFeature.h
View File

@ -26,8 +26,10 @@ public:
private:
Phrase m_words;
size_t m_target_length;
float m_ref_length;
size_t m_num_new_words;
size_t m_source_length;
float m_scaled_ref_length;
std::vector< size_t > m_ngram_counts;
std::vector< size_t > m_ngram_matches;
};