Source code for nltk.translate.gdfa

# -*- coding: utf-8 -*-
# Natural Language Toolkit: GDFA word alignment symmetrization
#
# Copyright (C) 2001-2018 NLTK Project
# Authors: Liling Tan
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

from collections import defaultdict

[docs]def grow_diag_final_and(srclen, trglen, e2f, f2e): """ This module symmetrisatizes the source-to-target and target-to-source word alignment output and produces, aka. GDFA algorithm (Koehn, 2005). Step 1: Find the intersection of the bidirectional alignment. Step 2: Search for additional neighbor alignment points to be added, given these criteria: (i) neighbor alignments points are not in the intersection and (ii) neighbor alignments are in the union. Step 3: Add all other alignment points thats not in the intersection, not in the neighboring alignments that met the criteria but in the original foward/backward alignment outputs. >>> forw = ('0-0 2-1 9-2 21-3 10-4 7-5 11-6 9-7 12-8 1-9 3-10 ' ... '4-11 17-12 17-13 25-14 13-15 24-16 11-17 28-18') >>> back = ('0-0 1-9 2-9 3-10 4-11 5-12 6-6 7-5 8-6 9-7 10-4 ' ... '11-6 12-8 13-12 15-12 17-13 18-13 19-12 20-13 ' ... '21-3 22-12 23-14 24-17 25-15 26-17 27-18 28-18') >>> srctext = ("この よう な ハロー 白色 わい 星 の L 関数 " ... "は L と 共 に 不連続 に 増加 する こと が " ... "期待 さ れる こと を 示し た 。") >>> trgtext = ("Therefore , we expect that the luminosity function " ... "of such halo white dwarfs increases discontinuously " ... "with the luminosity .") >>> srclen = len(srctext.split()) >>> trglen = len(trgtext.split()) >>> >>> gdfa = grow_diag_final_and(srclen, trglen, forw, back) >>> gdfa == sorted(set([(28, 18), (6, 6), (24, 17), (2, 1), (15, 12), (13, 12), ... (2, 9), (3, 10), (26, 17), (25, 15), (8, 6), (9, 7), (20, ... 13), (18, 13), (0, 0), (10, 4), (13, 15), (23, 14), (7, 5), ... (25, 14), (1, 9), (17, 13), (4, 11), (11, 17), (9, 2), (22, ... 12), (27, 18), (24, 16), (21, 3), (19, 12), (17, 12), (5, ... 12), (11, 6), (12, 8)])) True References: Koehn, P., A. Axelrod, A. Birch, C. Callison, M. Osborne, and D. Talbot. 2005. Edinburgh System Description for the 2005 IWSLT Speech Translation Evaluation. In MT Eval Workshop. :type srclen: int :param srclen: the number of tokens in the source language :type trglen: int :param trglen: the number of tokens in the target language :type e2f: str :param e2f: the forward word alignment outputs from source-to-target language (in pharaoh output format) :type f2e: str :param f2e: the backward word alignment outputs from target-to-source language (in pharaoh output format) :rtype: set(tuple(int)) :return: the symmetrized alignment points from the GDFA algorithm """ # Converts pharaoh text format into list of tuples. e2f = [tuple(map(int,a.split('-'))) for a in e2f.split()] f2e = [tuple(map(int,a.split('-'))) for a in f2e.split()] neighbors = [(-1,0),(0,-1),(1,0),(0,1),(-1,-1),(-1,1),(1,-1),(1,1)] alignment = set(e2f).intersection(set(f2e)) # Find the intersection. union = set(e2f).union(set(f2e)) # *aligned* is used to check if neighbors are aligned in grow_diag() aligned = defaultdict(set) for i,j in alignment: aligned['e'].add(i) aligned['f'].add(j) def grow_diag(): """ Search for the neighbor points and them to the intersected alignment points if criteria are met. """ prev_len = len(alignment) - 1 # iterate until no new points added while prev_len < len(alignment): no_new_points = True # for english word e = 0 ... en for e in range(srclen): # for foreign word f = 0 ... fn for f in range(trglen): # if ( e aligned with f) if (e,f) in alignment: # for each neighboring point (e-new, f-new) for neighbor in neighbors: neighbor = tuple(i+j for i,j in zip((e,f),neighbor)) e_new, f_new = neighbor # if ( ( e-new not aligned and f-new not aligned) # and (e-new, f-new in union(e2f, f2e) ) if (e_new not in aligned and f_new not in aligned)\ and neighbor in union: alignment.add(neighbor) aligned['e'].add(e_new); aligned['f'].add(f_new) prev_len+=1 no_new_points = False # iterate until no new points added if no_new_points: break def final_and(a): """ Adds remaining points that are not in the intersection, not in the neighboring alignments but in the original *e2f* and *f2e* alignments """ # for english word e = 0 ... en for e_new in range(srclen): # for foreign word f = 0 ... fn for f_new in range(trglen): # if ( ( e-new not aligned and f-new not aligned) # and (e-new, f-new in union(e2f, f2e) ) if (e_new not in aligned and f_new not in aligned and (e_new, f_new) in union): alignment.add((e_new, f_new)) aligned['e'].add(e_new); aligned['f'].add(f_new) grow_diag() final_and(e2f) final_and(f2e) return sorted(alignment)