# Natural Language Toolkit: GDFA word alignment symmetrization
#
# Copyright (C) 2001-2023 NLTK Project
# Authors: Liling Tan
# URL: <https://www.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 that are not in the intersection, not in
the neighboring alignments that met the criteria but in the original
forward/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)