mirror of
https://github.com/moses-smt/mosesdecoder.git
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b231ffc8b1
git-svn-id: https://mosesdecoder.svn.sourceforge.net/svnroot/mosesdecoder/trunk@1713 1f5c12ca-751b-0410-a591-d2e778427230
272 lines
7.3 KiB
C++
272 lines
7.3 KiB
C++
#ifndef __SCORER_H__
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#define __SCORER_H__
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#include <algorithm>
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#include <cmath>
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#include <iostream>
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#include <iterator>
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#include <set>
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#include <sstream>
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#include <stdexcept>
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#include <string>
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#include <vector>
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#include "Types.h"
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#include "ScoreData.h"
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using namespace std;
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class ScoreStats;
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/**
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* Superclass of all scorers and dummy implementation. In order to add a new
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* scorer it should be sufficient to override prepareStats(), setReferenceFiles()
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* and score() (or calculateScore()).
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**/
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class Scorer {
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public:
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Scorer(const string& name): _name(name), _scoreData(0),_preserveCase(false) {}
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/**
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* set the reference files. This must be called before prepareStats.
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**/
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virtual void setReferenceFiles(const vector<string>& referenceFiles) {
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//do nothing
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}
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/**
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* Process the given guessed text, corresponding to the given reference sindex
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* and add the appropriate statistics to the entry.
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**/
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virtual void prepareStats(int sindex, const string& text, ScoreStats& entry) {
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//cerr << text << std::endl;
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}
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/**
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* Score using each of the candidate index, then go through the diffs
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* applying each in turn, and calculating a new score each time.
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**/
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virtual void score(const candidates_t& candidates, const diffs_t& diffs,
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statscores_t& scores) {
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//dummy impl
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if (!_scoreData) {
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throw runtime_error("score data not loaded");
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}
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scores.push_back(0);
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for (size_t i = 0; i < diffs.size(); ++i) {
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scores.push_back(0);
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}
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}
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/**
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* Calculate the score of the sentences corresponding to the list of candidate
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* indices. Each index indicates the 1-best choice from the n-best list.
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**/
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float score(const candidates_t& candidates) {
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diffs_t diffs;
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statscores_t scores;
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score(candidates, diffs, scores);
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return scores[0];
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}
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const string& getName() const {return _name;}
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size_t getReferenceSize() {
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if (_scoreData) {
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return _scoreData->size();
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}
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return 0;
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}
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/**
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* Set the score data, prior to scoring.
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**/
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void setScoreData(ScoreData* scoreData) {
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_scoreData = scoreData;
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}
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protected:
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typedef map<string,int> encodings_t;
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typedef map<string,int>::iterator encodings_it;
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/**
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* Tokenise line and encode.
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* Note: We assume that all tokens are separated by single spaces
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**/
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void encode(const string& line, vector<int>& encoded) {
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//cerr << line << endl;
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istringstream in (line);
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string token;
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while (in >> token) {
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if (!_preserveCase) {
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for (string::iterator i = token.begin(); i != token.end(); ++i) {
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*i = tolower(*i);
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}
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}
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encodings_it encoding = _encodings.find(token);
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int encoded_token;
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if (encoding == _encodings.end()) {
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encoded_token = (int)_encodings.size();
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_encodings[token] = encoded_token;
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//cerr << encoded_token << "(n) ";
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} else {
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encoded_token = encoding->second;
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//cerr << encoded_token << " ";
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}
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encoded.push_back(encoded_token);
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}
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//cerr << endl;
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}
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ScoreData* _scoreData;
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encodings_t _encodings;
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bool _preserveCase;
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private:
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string _name;
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};
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/**
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* Abstract base class for scorers that work by adding statistics across all
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* outout sentences, then apply some formula, e.g. bleu, per. **/
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class StatisticsBasedScorer : public Scorer {
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public:
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StatisticsBasedScorer(const string& name): Scorer(name) {}
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virtual void score(const candidates_t& candidates, const diffs_t& diffs,
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statscores_t& scores);
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protected:
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//calculate the actual score
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virtual statscore_t calculateScore(const vector<int>& totals) = 0;
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};
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enum BleuReferenceLengthStrategy { AVERAGE, SHORTEST, CLOSEST };
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/**
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* Bleu scoring
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**/
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class BleuScorer: public StatisticsBasedScorer {
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public:
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BleuScorer() : StatisticsBasedScorer("BLEU"),_refLengthStrategy(SHORTEST) {}
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virtual void setReferenceFiles(const vector<string>& referenceFiles);
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virtual void prepareStats(int sid, const string& text, ScoreStats& entry);
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static const int LENGTH;
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protected:
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float calculateScore(const vector<int>& comps);
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private:
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//no copy
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BleuScorer(const BleuScorer&);
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BleuScorer& operator=(const BleuScorer&);
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//Used to construct the ngram map
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struct CompareNgrams {
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int operator() (const vector<int>& a, const vector<int>& b) {
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size_t i;
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size_t as = a.size();
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size_t bs = b.size();
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for (i = 0; i < as && i < bs; ++i) {
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if (a[i] < b[i]) {
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//cerr << "true" << endl;
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return true;
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}
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if (a[i] > b[i]) {
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//cerr << "false" << endl;
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return false;
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}
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}
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//entries are equal, shortest wins
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return as < bs;;
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}
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};
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typedef map<vector<int>,int,CompareNgrams> counts_t;
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typedef map<vector<int>,int,CompareNgrams>::iterator counts_it;
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typedef vector<counts_t*> refcounts_t;
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size_t countNgrams(const string& line, counts_t& counts, unsigned int n);
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void dump_counts(counts_t& counts) {
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for (counts_it i = counts.begin(); i != counts.end(); ++i) {
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cerr << "(";
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copy(i->first.begin(), i->first.end(), ostream_iterator<int>(cerr," "));
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cerr << ") " << i->second << ", ";
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}
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cerr << endl;
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}
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BleuReferenceLengthStrategy _refLengthStrategy;
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// data extracted from reference files
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refcounts_t _refcounts;
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vector<vector<size_t> > _reflengths;
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};
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/**
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* Implementation of position-independent word error rate. This is defined
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* as 1 - (correct - max(0,output_length - ref_length)) / ref_length
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* In fact, we ignore the " 1 - " so that it can be maximised.
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**/
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class PerScorer: public StatisticsBasedScorer {
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public:
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PerScorer() : StatisticsBasedScorer("PER") {}
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virtual void setReferenceFiles(const vector<string>& referenceFiles);
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virtual void prepareStats(int sid, const string& text, ScoreStats& entry);
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protected:
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virtual float calculateScore(const vector<int>& comps) ;
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private:
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//no copy
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PerScorer(const PerScorer&);
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PerScorer& operator=(const PerScorer&);
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// data extracted from reference files
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vector<size_t> _reflengths;
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vector<multiset<int> > _reftokens;
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};
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class ScorerFactory {
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public:
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vector<string> getTypes() {
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vector<string> types;
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types.push_back(string("BLEU"));
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types.push_back(string("PER"));
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return types;
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}
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Scorer* getScorer(const string& type) {
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if (type == "BLEU") {
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return new BleuScorer();
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} else if (type == "PER") {
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return new PerScorer();
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} else {
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throw runtime_error("Unknown scorer type: " + type);
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}
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}
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};
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#endif //__SCORER_H
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