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#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Scoring script for computing pairwise BLEU and multi-ref BLEU over a set of
candidate hypotheses.
See `"Mixture Models for Diverse Machine Translation: Tricks of the Trade"
(Shen et al., 2019) <https://arxiv.org/abs/1902.07816>`_.
"""
import argparse
import random
import sys
from itertools import chain
import numpy as np
from sacrebleu import compute_bleu, corpus_bleu as _corpus_bleu
def main():
parser = argparse.ArgumentParser(sys.argv[0])
parser.add_argument(
"--sys", nargs="*", default="", metavar="FILE", help="path to system output"
)
parser.add_argument("--ref", default="", metavar="FILE", help="path to references")
parser.add_argument(
"--output",
default="",
metavar="FILE",
help="print outputs into a pretty format",
)
args = parser.parse_args()
if args.sys:
src, tgt, hypos, log_probs = load_sys(args.sys)
print("pairwise BLEU: %.2f" % pairwise(hypos))
if args.output:
merge(src, tgt, hypos, log_probs, args.output)
if args.ref:
_, _, refs = load_ref(args.ref)
if args.sys:
multi_ref(refs, hypos)
else:
intra_ref(refs)
def dictolist(d):
a = sorted(d.items(), key=lambda i: i[0])
return [i[1] for i in a]
def load_sys(paths):
src, tgt, hypos, log_probs = {}, {}, {}, {}
for path in paths:
with open(path) as f:
for line in f:
line = line.rstrip()
# S: source
# T: target
# D: detokenized system output
if line.startswith(("S-", "T-", "D-")):
i = int(line[line.find("-") + 1 : line.find("\t")])
if line.startswith("S-"):
src[i] = line.split("\t")[1]
if line.startswith("T-"):
tgt[i] = line.split("\t")[1]
if line.startswith("D-"):
if i not in hypos:
hypos[i] = []
log_probs[i] = []
hypos[i].append(line.split("\t")[2])
log_probs[i].append(float(line.split("\t")[1]))
return dictolist(src), dictolist(tgt), dictolist(hypos), dictolist(log_probs)
def load_ref(path):
with open(path) as f:
lines = f.readlines()
src, tgt, refs = [], [], []
i = 0
while i < len(lines):
if lines[i].startswith("S-"):
src.append(lines[i].split("\t")[1].rstrip())
i += 1
elif lines[i].startswith("T-"):
tgt.append(lines[i].split("\t")[1].rstrip())
i += 1
else:
a = []
while i < len(lines) and lines[i].startswith("R"):
a.append(lines[i].split("\t")[1].rstrip())
i += 1
refs.append(a)
return src, tgt, refs
def merge(src, tgt, hypos, log_probs, path):
with open(path, "w") as f:
for s, t, hs, lps in zip(src, tgt, hypos, log_probs):
f.write(s + "\n")
f.write(t + "\n")
f.write("\n")
for h, lp in zip(hs, lps):
f.write("\t%f\t%s\n" % (lp, h.strip()))
f.write("------------------------------------------------------\n")
def corpus_bleu(sys_stream, ref_streams):
bleu = _corpus_bleu(sys_stream, ref_streams, tokenize="none")
return bleu.score
def sentence_bleu(hypothesis, reference):
bleu = _corpus_bleu(hypothesis, reference)
for i in range(1, 4):
bleu.counts[i] += 1
bleu.totals[i] += 1
bleu = compute_bleu(
bleu.counts,
bleu.totals,
bleu.sys_len,
bleu.ref_len,
smooth_method="exp",
)
return bleu.score
def pairwise(sents):
_ref, _hypo = [], []
for s in sents:
for i in range(len(s)):
for j in range(len(s)):
if i != j:
_ref.append(s[i])
_hypo.append(s[j])
return corpus_bleu(_hypo, [_ref])
def multi_ref(refs, hypos):
_ref, _hypo = [], []
ref_cnt = 0
assert len(refs) == len(hypos)
# count number of refs covered
for rs, hs in zip(refs, hypos):
a = set()
for h in hs:
s = [sentence_bleu(h, r) for r in rs]
j = np.argmax(s)
_ref.append(rs[j])
_hypo.append(h)
best = [k for k in range(len(rs)) if s[k] == s[j]]
a.add(random.choice(best))
ref_cnt += len(a)
print("#refs covered: %.2f" % (ref_cnt / len(refs)))
# transpose refs and hypos
refs = list(zip(*refs))
hypos = list(zip(*hypos))
# compute multi-ref corpus BLEU (leave-one-out to be comparable to intra_ref)
k = len(hypos)
m = len(refs)
flat_hypos = [hypos[j][i] for i in range(len(hypos[0])) for j in range(k)]
duplicated_refs = [[ref for ref in refs_i for _ in range(k)] for refs_i in refs]
loo_bleus = []
for held_out_ref in range(m):
remaining_refs = (
duplicated_refs[:held_out_ref] + duplicated_refs[held_out_ref + 1 :]
)
assert len(remaining_refs) == m - 1
loo_bleus.append(corpus_bleu(flat_hypos, remaining_refs))
print("average multi-reference BLEU (leave-one-out): %.2f" % np.mean(loo_bleus))
def intra_ref(refs):
print("ref pairwise BLEU: %.2f" % pairwise(refs))
refs = list(zip(*refs))
m = len(refs)
concat_h = []
concat_rest = [[] for j in range(m - 1)]
for i, h in enumerate(refs):
rest = refs[:i] + refs[i + 1 :]
concat_h.append(h)
for j in range(m - 1):
concat_rest[j].extend(rest[j])
concat_h = list(chain.from_iterable(concat_h))
bleu = corpus_bleu(concat_h, concat_rest)
print("multi-reference BLEU (leave-one-out): %.2f" % bleu)
if __name__ == "__main__":
main()
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