path: root/moses/src/BleuScoreFeature.cpp

#include "BleuScoreFeature.h"

#include "StaticData.h"

using namespace std;

namespace Moses {

size_t BleuScoreState::bleu_order = 4;

BleuScoreState::BleuScoreState(): m_words(1),
                                  m_source_length(0),
                                  m_target_length(0),
                                  m_scaled_ref_length(0),
                                  m_ngram_counts(bleu_order),
                                  m_ngram_matches(bleu_order)
{
}

int BleuScoreState::Compare(const FFState& o) const
{
    if (&o == this)
      return 0;

    const StaticData &staticData = StaticData::Instance();
    SearchAlgorithm searchAlgorithm = staticData.GetSearchAlgorithm();
    bool chartDecoding = (searchAlgorithm == ChartDecoding);
    if (chartDecoding) 
      return 0;

    const BleuScoreState& other = dynamic_cast<const BleuScoreState&>(o);
    int c = m_words.Compare(other.m_words);
    if (c != 0)
      return c;

    /*for(size_t i = 0; i < m_ngram_counts.size(); i++) {
      if (m_ngram_counts[i] < other.m_ngram_counts[i])
	return -1;
      if (m_ngram_counts[i] > other.m_ngram_counts[i])
	return 1;
      if (m_ngram_matches[i] < other.m_ngram_matches[i])
	return -1;
      if (m_ngram_matches[i] > other.m_ngram_matches[i])
	return 1;
	}*/

    return 0;
}
std::ostream& operator<<(std::ostream& out, const BleuScoreState& state) {
  state.print(out);
  return out;
}

void BleuScoreState::print(std::ostream& out) const {
  out << "ref=" << m_scaled_ref_length
  	<< ";source=" << m_source_length
	  << ";target=" << m_target_length << ";counts=";
  for (size_t i = 0; i < bleu_order; ++i) {
    out << m_ngram_matches[i] << "/" << m_ngram_counts[i] << ",";
  }
  out << "ctxt=" << m_words;
    
}

void BleuScoreState::AddNgramCountAndMatches(std::vector< size_t >& counts,
																									std::vector< size_t >& matches) {
	for (size_t order = 0; order < BleuScoreState::bleu_order; ++order) {
		m_ngram_counts[order] += counts[order];
		m_ngram_matches[order] += matches[order];
	}
}

void BleuScoreFeature::PrintHistory(std::ostream& out) const {
	out << "source length history=" << m_source_length_history << endl;
	out << "target length history=" << m_target_length_history << endl;
	out << "ref length history=" << m_ref_length_history << endl;

  for (size_t i = 0; i < BleuScoreState::bleu_order; ++i) {
    out << "match history/count history (" << i << "):" << m_match_history[i] << "/" << m_count_history[i] << endl;
  }
}

void BleuScoreFeature::SetBleuParameters(bool disable, bool sentenceBleu, bool scaleByInputLength, bool scaleByAvgInputLength,
		bool scaleByInverseLength, bool scaleByAvgInverseLength,
		float scaleByX, float historySmoothing, size_t scheme) {
	m_enabled = !disable;
	m_sentence_bleu = sentenceBleu;
	m_scale_by_input_length = scaleByInputLength;
	m_scale_by_avg_input_length = scaleByAvgInputLength;
	m_scale_by_inverse_length = scaleByInverseLength;
	m_scale_by_avg_inverse_length = scaleByAvgInverseLength;
	m_scale_by_x = scaleByX;
	m_historySmoothing = historySmoothing;
	m_smoothing_scheme = (SmoothingScheme)scheme;
}

// Incoming references (refs) are stored as refs[file_id][[sent_id][reference]]
// This data structure: m_refs[sent_id][[vector<length>][ngrams]]
void BleuScoreFeature::LoadReferences(const std::vector< std::vector< std::string > >& refs)
{
	m_refs.clear();
	FactorCollection& fc = FactorCollection::Instance();
	for (size_t file_id = 0; file_id < refs.size(); file_id++) {
		for (size_t sent_id = 0; sent_id < refs[file_id].size(); sent_id++) {
			const string& ref = refs[file_id][sent_id];
			vector<string> refTokens  = Tokenize(ref);
			if (file_id == 0)
				m_refs[sent_id] = pair<vector<size_t>,NGrams>();
			pair<vector<size_t>,NGrams>& ref_pair = m_refs[sent_id];
			(ref_pair.first).push_back(refTokens.size());
			for (size_t order = 1; order <= BleuScoreState::bleu_order; order++) {
				for (size_t end_idx = order; end_idx <= refTokens.size(); end_idx++) {
					Phrase ngram(1);
					for (size_t s_idx = end_idx - order; s_idx < end_idx; s_idx++) {
						const Factor* f = fc.AddFactor(Output, 0, refTokens[s_idx]);
						Word w;
						w.SetFactor(0, f);
						ngram.AddWord(w);
					}
					ref_pair.second[ngram] += 1;
				}
			}
   	}
	}

//	cerr << "Number of ref files: " << refs.size() << endl;
//	for (size_t i = 0; i < m_refs.size(); ++i) {
//		cerr << "Sent id " << i << ", number of references: " << (m_refs[i].first).size() << endl;
//	}
}

void BleuScoreFeature::SetCurrSourceLength(size_t source_length) {
    m_cur_source_length = source_length;
}
void BleuScoreFeature::SetCurrNormSourceLength(size_t source_length) {
    m_cur_norm_source_length = source_length;
}

// m_refs[sent_id][[vector<length>][ngrams]]
void BleuScoreFeature::SetCurrShortestRefLength(size_t sent_id) {
		// look for shortest reference
		int shortestRef = -1;
		for (size_t i = 0; i < (m_refs[sent_id].first).size(); ++i) {
			if (shortestRef == -1 || (m_refs[sent_id].first)[i] < shortestRef)
				shortestRef = (m_refs[sent_id].first)[i];
		}
		m_cur_ref_length = shortestRef;
//		cerr << "Set shortest cur_ref_length: " << m_cur_ref_length << endl;
}

void BleuScoreFeature::SetCurrAvgRefLength(size_t sent_id) {
		// compute average reference length
		size_t sum = 0;
		size_t numberRefs = (m_refs[sent_id].first).size();
		for (size_t i = 0; i < numberRefs; ++i) {
			sum += (m_refs[sent_id].first)[i];
		}
		m_cur_ref_length = (float)sum/numberRefs;
//		cerr << "Set average cur_ref_length: " << m_cur_ref_length << endl;
}

void BleuScoreFeature::SetCurrReferenceNgrams(size_t sent_id) {
    m_cur_ref_ngrams = m_refs[sent_id].second;
}

size_t BleuScoreFeature::GetShortestRefIndex(size_t ref_id) {
		// look for shortest reference
		int shortestRef = -1;
		size_t shortestRefIndex = 0;
		for (size_t i = 0; i < (m_refs[ref_id].first).size(); ++i) {
			if (shortestRef == -1 || (m_refs[ref_id].first)[i] < shortestRef) {
				shortestRef = (m_refs[ref_id].first)[i];
				shortestRefIndex = i;
			}
		}
    return shortestRefIndex;
}

/*
 * Update the pseudo-document O after each translation of a source sentence.
 * (O is an exponentially-weighted moving average of vectors c(e;{r_k}))
 * O = m_historySmoothing * (O + c(e_oracle))
 * O_f = m_historySmoothing * (O_f + |f|)		input length of pseudo-document
 */
void BleuScoreFeature::UpdateHistory(const vector< const Word* >& hypo) {
    Phrase phrase(hypo);
    std::vector< size_t > ngram_counts(BleuScoreState::bleu_order);
    std::vector< size_t > ngram_matches(BleuScoreState::bleu_order);

    // compute vector c(e;{r_k}):
    // vector of effective reference length, number of ngrams in e, number of ngram matches between e and r_k
    GetNgramMatchCounts(phrase, m_cur_ref_ngrams, ngram_counts, ngram_matches, 0);

    // update counts and matches for every ngram length with counts from hypo
    for (size_t i = 0; i < BleuScoreState::bleu_order; i++) {
        m_count_history[i] = m_historySmoothing * (m_count_history[i] + ngram_counts[i]);
        m_match_history[i] = m_historySmoothing * (m_match_history[i] + ngram_matches[i]);
    }

    // update counts for reference and target length
    m_source_length_history = m_historySmoothing * (m_source_length_history + m_cur_source_length);
    m_target_length_history = m_historySmoothing * (m_target_length_history + hypo.size());
    m_ref_length_history = m_historySmoothing * (m_ref_length_history + m_cur_ref_length);
}

/*
 * Update history with a batch of translations
 */
void BleuScoreFeature::UpdateHistory(const vector< vector< const Word* > >& hypos, vector<size_t>& sourceLengths, vector<size_t>& ref_ids, size_t rank, size_t epoch) {
	for (size_t ref_id = 0; ref_id < hypos.size(); ++ref_id){
	    Phrase phrase(hypos[ref_id]);
	    std::vector< size_t > ngram_counts(BleuScoreState::bleu_order);
	    std::vector< size_t > ngram_matches(BleuScoreState::bleu_order);

	    // set current source and reference information for each oracle in the batch
	    size_t cur_source_length = sourceLengths[ref_id];
	    size_t hypo_length = hypos[ref_id].size();
	    size_t cur_ref_length = GetClosestRefLength(ref_ids[ref_id], hypo_length);
	    NGrams cur_ref_ngrams = m_refs[ref_ids[ref_id]].second;
	    cerr << "reference length: " << cur_ref_length << endl;

	    // compute vector c(e;{r_k}):
	    // vector of effective reference length, number of ngrams in e, number of ngram matches between e and r_k
	    GetNgramMatchCounts(phrase, cur_ref_ngrams, ngram_counts, ngram_matches, 0);

	    // update counts and matches for every ngram length with counts from hypo
	    for (size_t i = 0; i < BleuScoreState::bleu_order; i++) {
	        m_count_history[i] += ngram_counts[i];
	        m_match_history[i] += ngram_matches[i];

	        // do this for last position in batch
	        if (ref_id == hypos.size() - 1) {
	        	m_count_history[i] *= m_historySmoothing;
	        	m_match_history[i] *= m_historySmoothing;
	        }
	    }

	    // update counts for reference and target length
	    m_source_length_history += cur_source_length;
	    m_target_length_history += hypos[ref_id].size();
	    m_ref_length_history += cur_ref_length;

	    // do this for last position in batch
	    if (ref_id == hypos.size() - 1) {
	    	cerr << "Rank " << rank << ", epoch " << epoch << " ,source length history: " << m_source_length_history << " --> " << m_source_length_history * m_historySmoothing << endl;
	    	cerr << "Rank " << rank << ", epoch " << epoch << " ,target length history: " << m_target_length_history << " --> " << m_target_length_history * m_historySmoothing << endl;
	    	m_source_length_history *= m_historySmoothing;
	    	m_target_length_history *= m_historySmoothing;
	    	m_ref_length_history *= m_historySmoothing;
	    }
	}
}

/*
 * Print batch of reference translations
 */
/*void BleuScoreFeature::PrintReferenceLength(const vector<size_t>& ref_ids) {
	for (size_t ref_id = 0; ref_id < ref_ids.size(); ++ref_id){
	    size_t cur_ref_length = (m_refs[ref_ids[ref_id]].first)[0]; // TODO!!
	    cerr << "reference length: " << cur_ref_length << endl;
	}
}*/

size_t BleuScoreFeature::GetClosestRefLength(size_t ref_id, int hypoLength) {
	// look for closest reference
	int currentDist = -1;
	int closestRefLength = -1;
	for (size_t i = 0; i < (m_refs[ref_id].first).size(); ++i) {
		if (closestRefLength == -1 || abs(hypoLength - (int)(m_refs[ref_id].first)[i]) < currentDist) {
			closestRefLength = (m_refs[ref_id].first)[i];
			currentDist = abs(hypoLength - (int)(m_refs[ref_id].first)[i]);
		}
	}
	return (size_t)closestRefLength;
}

/*
 * Given a phrase (current translation) calculate its ngram counts and
 * its ngram matches against the ngrams in the reference translation
 */
void BleuScoreFeature::GetNgramMatchCounts(Phrase& phrase,
                                           const NGrams& ref_ngram_counts,
                                           std::vector< size_t >& ret_counts,
                                           std::vector< size_t >& ret_matches,
                                           size_t skip_first) const
{
    NGrams::const_iterator ref_ngram_counts_iter;
    size_t ngram_start_idx, ngram_end_idx;

    // Chiang et al (2008) use unclipped counts of ngram matches
    for (size_t end_idx = skip_first; end_idx < phrase.GetSize(); end_idx++) {
        for (size_t order = 0; order < BleuScoreState::bleu_order; order++) {
            if (order > end_idx) break;

            ngram_end_idx = end_idx;
            ngram_start_idx = end_idx - order;

            Phrase ngram = phrase.GetSubString(WordsRange(ngram_start_idx, ngram_end_idx), 0);
            ret_counts[order]++;

            ref_ngram_counts_iter = ref_ngram_counts.find(ngram);
            if (ref_ngram_counts_iter != ref_ngram_counts.end())
                ret_matches[order]++;
        }
    }
}

// score ngrams of words that have been added before the previous word span
void BleuScoreFeature::GetNgramMatchCounts_prefix(Phrase& phrase,
                                           const NGrams& ref_ngram_counts,
                                           std::vector< size_t >& ret_counts,
                                           std::vector< size_t >& ret_matches,
                                           size_t new_start_indices,
                                           size_t last_end_index) const
{
    NGrams::const_iterator ref_ngram_counts_iter;
    size_t ngram_start_idx, ngram_end_idx;

    // Chiang et al (2008) use unclipped counts of ngram matches
    for (size_t start_idx = 0; start_idx < new_start_indices; start_idx++) {
        for (size_t order = 0; order < BleuScoreState::bleu_order; order++) {
        		ngram_start_idx = start_idx;
            ngram_end_idx = start_idx + order;
            if (order > ngram_end_idx) break;
            if (ngram_end_idx > last_end_index) break;

            Phrase ngram = phrase.GetSubString(WordsRange(ngram_start_idx, ngram_end_idx), 0);
            ret_counts[order]++;

            ref_ngram_counts_iter = ref_ngram_counts.find(ngram);
            if (ref_ngram_counts_iter != ref_ngram_counts.end())
                ret_matches[order]++;
        }
    }
}

// score ngrams around the overlap of two previously scored phrases
void BleuScoreFeature::GetNgramMatchCounts_overlap(Phrase& phrase,
                                           const NGrams& ref_ngram_counts,
                                           std::vector< size_t >& ret_counts,
                                           std::vector< size_t >& ret_matches,
                                           size_t overlap_index) const
{
    NGrams::const_iterator ref_ngram_counts_iter;
    size_t ngram_start_idx, ngram_end_idx;

    // Chiang et al (2008) use unclipped counts of ngram matches
    for (size_t end_idx = overlap_index; end_idx < phrase.GetSize(); end_idx++) {
    	if (end_idx >= (overlap_index+BleuScoreState::bleu_order-1)) break;
    	for (size_t order = 0; order < BleuScoreState::bleu_order; order++) {
            if (order > end_idx) break;

            ngram_end_idx = end_idx;
            ngram_start_idx = end_idx - order;
            if (ngram_start_idx >= overlap_index) continue; // only score ngrams that span the overlap point

            Phrase ngram = phrase.GetSubString(WordsRange(ngram_start_idx, ngram_end_idx), 0);
            ret_counts[order]++;

            ref_ngram_counts_iter = ref_ngram_counts.find(ngram);
            if (ref_ngram_counts_iter != ref_ngram_counts.end())
                ret_matches[order]++;
        }
    }
}

void BleuScoreFeature::GetClippedNgramMatchesAndCounts(Phrase& phrase,
                                           const NGrams& ref_ngram_counts,
                                           std::vector< size_t >& ret_counts,
                                           std::vector< size_t >& ret_matches,
                                           size_t skip_first) const
{
	NGrams::const_iterator ref_ngram_counts_iter;
	size_t ngram_start_idx, ngram_end_idx;

	Matches ngram_matches;
	for (size_t end_idx = skip_first; end_idx < phrase.GetSize(); end_idx++) {
		for (size_t order = 0; order < BleuScoreState::bleu_order; order++) {
			if (order > end_idx) break;

			ngram_end_idx = end_idx;
			ngram_start_idx = end_idx - order;

			Phrase ngram = phrase.GetSubString(WordsRange(ngram_start_idx, ngram_end_idx), 0);
			ret_counts[order]++;

			ref_ngram_counts_iter = ref_ngram_counts.find(ngram);
			if (ref_ngram_counts_iter != ref_ngram_counts.end()) {
				ngram_matches[order][ngram]++;
			}
		}
	}

	// clip ngram matches
	for (size_t order = 0; order < BleuScoreState::bleu_order; order++) {
		NGrams::const_iterator iter;

		// iterate over ngram counts for every ngram order
		for (iter=ngram_matches[order].begin(); iter != ngram_matches[order].end(); ++iter) {
			ref_ngram_counts_iter = ref_ngram_counts.find(iter->first);
			if (iter->second > ref_ngram_counts_iter->second) {
				ret_matches[order] += ref_ngram_counts_iter->second;
			}
			else {
				ret_matches[order] += iter->second;
			}
    }
	}
}

/*
 * Given a previous state, compute Bleu score for the updated state with an additional target
 * phrase translated.
 */
FFState* BleuScoreFeature::Evaluate(const Hypothesis& cur_hypo, 
                                    const FFState* prev_state, 
                                    ScoreComponentCollection* accumulator) const
{
	if (!m_enabled) return new BleuScoreState();
	
	NGrams::const_iterator reference_ngrams_iter;
    const BleuScoreState& ps = dynamic_cast<const BleuScoreState&>(*prev_state);
    BleuScoreState* new_state = new BleuScoreState(ps);
    
    float old_bleu, new_bleu;
    size_t num_new_words, ctx_start_idx, ctx_end_idx;

    // Calculate old bleu;
    old_bleu = CalculateBleu(new_state);

    // Get context and append new words.
    num_new_words = cur_hypo.GetCurrTargetLength();
    if (num_new_words == 0) {
    	return new_state;
    }
 
    Phrase new_words = ps.m_words;
    new_words.Append(cur_hypo.GetCurrTargetPhrase());
    //cerr << "NW: " << new_words << endl;

    // get ngram matches for new words
    GetNgramMatchCounts(new_words,
                        m_cur_ref_ngrams,
                        new_state->m_ngram_counts,
                        new_state->m_ngram_matches,
                        new_state->m_words.GetSize()); // number of words in previous states

    // Update state variables
    ctx_end_idx = new_words.GetSize()-1;
    size_t bleu_context_length = BleuScoreState::bleu_order -1;
    if (ctx_end_idx > bleu_context_length) {
      ctx_start_idx = ctx_end_idx - bleu_context_length;
    } else {
      ctx_start_idx = 0;
    }

    WordsBitmap coverageVector = cur_hypo.GetWordsBitmap();
    new_state->m_source_length = coverageVector.GetNumWordsCovered();

    new_state->m_words = new_words.GetSubString(WordsRange(ctx_start_idx,
                                                           ctx_end_idx));
    new_state->m_target_length += cur_hypo.GetCurrTargetLength();

    // we need a scaled reference length to compare the current target phrase to the corresponding reference phrase
    new_state->m_scaled_ref_length = m_cur_ref_length * 
        ((float)coverageVector.GetNumWordsCovered()/coverageVector.GetSize());

    // Calculate new bleu.
    new_bleu = CalculateBleu(new_state);

    // Set score to new Bleu score
    accumulator->PlusEquals(this, new_bleu - old_bleu);
    return new_state;
}

FFState* BleuScoreFeature::EvaluateChart(const ChartHypothesis& cur_hypo, int featureID,
		ScoreComponentCollection* accumulator ) const {
  if (!m_enabled) return new BleuScoreState();
	
  NGrams::const_iterator reference_ngrams_iter;
  
  const Phrase& curr_target_phrase = static_cast<const Phrase&>(cur_hypo.GetCurrTargetPhrase());
//  cerr << "\nCur target phrase: " << cur_hypo.GetTargetLHS() << " --> " << curr_target_phrase << endl;

  // Calculate old bleu of previous states
  float old_bleu = 0, new_bleu = 0;
  size_t num_old_words = 0, num_words_first_prev = 0;
  size_t num_words_added_left = 0, num_words_added_right = 0;

  // double-check cases where more than two previous hypotheses were combined
  assert(cur_hypo.GetPrevHypos().size() <= 2);
  BleuScoreState* new_state;
  if (cur_hypo.GetPrevHypos().size() == 0)
  	new_state = new BleuScoreState();
  else {
  	const FFState* prev_state_zero = cur_hypo.GetPrevHypo(0)->GetFFState(featureID);
  	const BleuScoreState& ps_zero = dynamic_cast<const BleuScoreState&>(*prev_state_zero);
  	new_state = new BleuScoreState(ps_zero);
  	num_words_first_prev = ps_zero.m_target_length;

  	for (size_t i = 0; i < cur_hypo.GetPrevHypos().size(); ++i) {
  		const FFState* prev_state = cur_hypo.GetPrevHypo(i)->GetFFState(featureID);
  		const BleuScoreState* ps = dynamic_cast<const BleuScoreState*>(prev_state);
  		BleuScoreState* ps_nonConst = const_cast<BleuScoreState*>(ps);
//  		cerr << "prev phrase: " << cur_hypo.GetPrevHypo(i)->GetOutputPhrase()
//  				<< " ( " << cur_hypo.GetPrevHypo(i)->GetTargetLHS() << ")" << endl;

  		old_bleu += CalculateBleu(ps_nonConst);
  		num_old_words += ps->m_target_length;

  		if (i > 0)
  			// add ngram matches from other previous states
  			new_state->AddNgramCountAndMatches(ps_nonConst->m_ngram_counts, ps_nonConst->m_ngram_matches);
  	}
  }
  
  // check if we are already done (don't add <s> and </s>)
  size_t numWordsCovered = cur_hypo.GetCurrSourceRange().GetNumWordsCovered();
  if (numWordsCovered == m_cur_source_length) {
	  // Bleu score stays the same, do not need to add anything
	  //accumulator->PlusEquals(this, 0);
	  return new_state;
  }

  // set new context
  Phrase new_words = cur_hypo.GetOutputPhrase();
  new_state->m_words = new_words;
  size_t num_curr_words = new_words.GetSize();

  // get ngram matches for new words
  if (num_old_words == 0) {
//  	cerr << "compute right ngram context" << endl;
  	GetNgramMatchCounts(new_words,
  											m_cur_ref_ngrams,
  											new_state->m_ngram_counts,
  											new_state->m_ngram_matches,
  											0);
  }
  else if (new_words.GetSize() == num_old_words) {
  	// two hypotheses were glued together, compute new ngrams on the basis of first hypothesis
  	num_words_added_right = num_curr_words - num_words_first_prev;
  	// score around overlap point
//  	cerr << "compute overlap ngram context (" << (num_words_first_prev) << ")" << endl;
  	GetNgramMatchCounts_overlap(new_words,
  											m_cur_ref_ngrams,
  											new_state->m_ngram_counts,
  											new_state->m_ngram_matches,
  											num_words_first_prev);
  }
  else if (num_old_words + curr_target_phrase.GetNumTerminals() == num_curr_words) {
  	assert(curr_target_phrase.GetSize() == curr_target_phrase.GetNumTerminals()+1);
  	// previous hypothesis + rule with 1 non-terminal were combined (NT substituted by Ts)
  	for (size_t i = 0; i < curr_target_phrase.GetSize(); ++i)
  		if (curr_target_phrase.GetWord(i).IsNonTerminal()) {
  			num_words_added_left = i;
  			num_words_added_right = curr_target_phrase.GetSize() - (i+1);
  			break;
  		}

  	// left context
//  	cerr << "compute left ngram context" << endl;
  	if (num_words_added_left > 0)
  		GetNgramMatchCounts_prefix(new_words,
  											m_cur_ref_ngrams,
  											new_state->m_ngram_counts,
  											new_state->m_ngram_matches,
  											num_words_added_left,
  											num_curr_words - num_words_added_right - 1);

  	// right context
//  	cerr << "compute right ngram context" << endl;
  	if (num_words_added_right > 0)
  		GetNgramMatchCounts(new_words,
  											m_cur_ref_ngrams,
  											new_state->m_ngram_counts,
  											new_state->m_ngram_matches,
  											num_words_added_left + num_old_words);
  }
  else {
  	cerr << "undefined state.. " << endl;
  	exit(1);
  }

  // Update state variables
  size_t ctx_start_idx = 0;
  size_t ctx_end_idx = new_words.GetSize()-1;
  size_t bleu_context_length = BleuScoreState::bleu_order -1;
  if (ctx_end_idx > bleu_context_length) {
    ctx_start_idx = ctx_end_idx - bleu_context_length;
  }

  new_state->m_source_length = cur_hypo.GetCurrSourceRange().GetNumWordsCovered();
  new_state->m_words = new_words.GetSubString(WordsRange(ctx_start_idx, ctx_end_idx));
  new_state->m_target_length = cur_hypo.GetOutputPhrase().GetSize();

  // we need a scaled reference length to compare the current target phrase to the corresponding
  // reference phrase
  size_t cur_source_length = m_cur_source_length;
  new_state->m_scaled_ref_length = m_cur_ref_length * (float(new_state->m_source_length)/cur_source_length);
  
  // Calculate new bleu.
  new_bleu = CalculateBleu(new_state);

  // Set score to new Bleu score
  accumulator->PlusEquals(this, new_bleu - old_bleu);
  return new_state;
}

/**
 * Calculate real sentence Bleu score of complete translation
 */
float BleuScoreFeature::CalculateBleu(Phrase translation) const
{
	if (translation.GetSize() == 0)
	  return 0.0;
	
	Phrase normTranslation = translation;
    // remove start and end symbol for chart decoding
    if (m_cur_source_length != m_cur_norm_source_length) {
      WordsRange* range = new WordsRange(1, translation.GetSize()-2);
      normTranslation = translation.GetSubString(*range);
    }
    
    // get ngram matches for translation
    BleuScoreState* state = new BleuScoreState();
    GetClippedNgramMatchesAndCounts(normTranslation,
                        m_cur_ref_ngrams,
                        state->m_ngram_counts,
                        state->m_ngram_matches,
                        0); // number of words in previous states

    // set state variables
    state->m_words = normTranslation;
    state->m_source_length = m_cur_norm_source_length;
    state->m_target_length = normTranslation.GetSize();
    state->m_scaled_ref_length = m_cur_ref_length;

    // Calculate bleu.
    return CalculateBleu(state);
}

/*
 * Calculate Bleu score for a partial hypothesis given as state.
 */
float BleuScoreFeature::CalculateBleu(BleuScoreState* state) const {
  if (!state->m_ngram_counts[0]) return 0;
  if (!state->m_ngram_matches[0]) return 0;      	// if we have no unigram matches, score should be 0

  float precision = 1.0;
  float smooth = 1;
  float smoothed_count, smoothed_matches;
  
  if (m_sentence_bleu) {
    // Calculate geometric mean of modified ngram precisions
    // BLEU = BP * exp(SUM_1_4 1/4 * log p_n)
    // 		= BP * 4th root(PRODUCT_1_4 p_n)
    for (size_t i = 0; i < BleuScoreState::bleu_order; i++) {
      if (state->m_ngram_counts[i]) {
      	smoothed_matches = state->m_ngram_matches[i];
      	smoothed_count = state->m_ngram_counts[i];

      	switch (m_smoothing_scheme) {
      	case PLUS_ONE:
      	default:
      		if (i > 0) {
      			// smoothing for all n > 1
      			smoothed_matches += 1;
      			smoothed_count += 1;
      		}
      		break;
      	case PLUS_POINT_ONE:
      		if (i > 0) {
      			// smoothing for all n > 1
      			smoothed_matches += 0.1;
      			smoothed_count += 0.1;
      		}
      		break;
      	case PAPINENI:
      		if (state->m_ngram_matches[i] == 0) {
      			smooth *= 0.5;
      			smoothed_matches += smooth;
      			smoothed_count += smooth;
      		}
      		break;
      	}

        precision *= smoothed_matches / smoothed_count;
      }
    }

    // take geometric mean
    precision = pow(precision, (float)1/4);

    // Apply brevity penalty if applicable.
    // BP = 1 				if c > r
    // BP = e^(1- r/c))		if c <= r
    // where
    // c: length of the candidate translation
    // r: effective reference length (sum of best match lengths for each candidate sentence)
  	if (state->m_target_length < state->m_scaled_ref_length) {
  		float smoothed_target_length = m_target_length_history + state->m_target_length;
  		float smoothed_ref_length = m_ref_length_history + state->m_scaled_ref_length;
  		precision *= exp(1 - (smoothed_ref_length/ smoothed_target_length));
  	}

  	//cerr << "precision: " << precision << endl;

  	// Approximate bleu score as of Chiang/Resnik is scaled by the size of the input:
  	// B(e;f,{r_k}) = (O_f + |f|) * BLEU(O + c(e;{r_k}))
  	// where c(e;) is a vector of reference length, ngram counts and ngram matches
  	if (m_scale_by_input_length) {
  		precision *= m_cur_norm_source_length;
  	}
  	else if (m_scale_by_avg_input_length) {
  		precision *= m_avg_input_length;
  	}
  	else if (m_scale_by_inverse_length) {
  		precision *= (100/m_cur_norm_source_length);
  	}
  	else if (m_scale_by_avg_inverse_length) {
  		precision *= (100/m_avg_input_length);
  	}

  	return precision * m_scale_by_x;
  }
  else {
    // Revised history BLEU: compute Bleu in the context of the pseudo-document
    // B(b) = size_of_oracle_doc * (Bleu(B_hist + b) - Bleu(B_hist))
    // Calculate geometric mean of modified ngram precisions
    // BLEU = BP * exp(SUM_1_4 1/4 * log p_n)
    // 		= BP * 4th root(PRODUCT_1_4 p_n)
    for (size_t i = 0; i < BleuScoreState::bleu_order; i++) {
      if (state->m_ngram_counts[i]) {
      	smoothed_matches = m_match_history[i] + state->m_ngram_matches[i] + 0.1;
      	smoothed_count = m_count_history[i] + state->m_ngram_counts[i] + 0.1;
      	precision *= smoothed_matches / smoothed_count;
      }
    }

    // take geometric mean
    precision = pow(precision, (float)1/4);

    // Apply brevity penalty if applicable.
    if (m_target_length_history + state->m_target_length < m_ref_length_history + state->m_scaled_ref_length)
  	  precision *= exp(1 - (m_ref_length_history + state->m_scaled_ref_length/m_target_length_history + state->m_target_length));

    //cerr << "\nprecision: " << precision << endl;

    // **BLEU score of pseudo-document**
    float precision_pd = 1.0;
    if (m_target_length_history > 0) {
    	for (size_t i = 0; i < BleuScoreState::bleu_order; i++)
	  if (m_count_history[i] != 0)
	    precision_pd *= (m_match_history[i] + 0.1)/(m_count_history[i] + 0.1);

      // take geometric mean
      precision_pd = pow(precision_pd, (float)1/4);

      // Apply brevity penalty if applicable.
      if (m_target_length_history < m_ref_length_history)
    	  precision_pd *= exp(1 - (m_ref_length_history/m_target_length_history));
    }
    else
      precision_pd = 0;
    // **end BLEU of pseudo-document**

    //cerr << "precision pd: " << precision_pd << endl;

    float sentence_impact;
    if (m_target_length_history > 0) 
      sentence_impact = m_target_length_history * (precision - precision_pd);    
    else
      sentence_impact = precision;

    //cerr << "sentence impact: " << sentence_impact << endl;
    return sentence_impact * m_scale_by_x;
  }
}

const FFState* BleuScoreFeature::EmptyHypothesisState(const InputType& input) const
{
    return new BleuScoreState();
}

} // namespace.