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import os
from copy import deepcopy
import datasets
_CITATION = """\
@inproceedings{yannakoudakis-etal-2011-new,
title = "A New Dataset and Method for Automatically Grading {ESOL} Texts",
author = "Yannakoudakis, Helen and
Briscoe, Ted and
Medlock, Ben",
booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2011",
url = "https://aclanthology.org/P11-1019",
pages = "180--189",
}
"""
_DESCRIPTION = """\
The CLC FCE Dataset is a set of 1,244 exam scripts written by candidates sitting the Cambridge ESOL First Certificate
in English (FCE) examination in 2000 and 2001. The dataset exposes the sentence-level pre-tokenized M2 version, totaling
33236 sentences.
"""
_HOMEPAGE = ""
_LICENSE = "Custom, allowed for non-commercial research and educational purposes"
_URLS = {
"clc_fce_bea19": "https://www.cl.cam.ac.uk/research/nl/bea2019st/data/fce_v2.1.bea19.tar.gz"
}
class CLCFCE(datasets.GeneratorBasedBuilder):
"""Cambridge Learner Corpus: First Certificate in English"""
VERSION = datasets.Version("2.1.0")
def _info(self):
features = datasets.Features(
{
"src_tokens": datasets.Sequence(datasets.Value("string")),
"tgt_tokens": datasets.Sequence(datasets.Value("string")),
"corrections": [{
"idx_src": datasets.Sequence(datasets.Value("int32")),
"idx_tgt": datasets.Sequence(datasets.Value("int32")),
"corr_type": datasets.Value("string")
}]
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _URLS["clc_fce_bea19"]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"file_path": os.path.join(data_dir, "fce", "m2", "fce.train.gold.bea19.m2")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"file_path": os.path.join(data_dir, "fce", "m2", "fce.dev.gold.bea19.m2")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"file_path": os.path.join(data_dir, "fce", "m2", "fce.test.gold.bea19.m2")},
),
]
def _generate_examples(self, file_path):
skip_edits = {"noop", "UNK", "Um"}
with open(file_path, "r", encoding="utf-8") as f:
idx_ex = 0
src_sent, tgt_sent, corrections, offset = None, None, [], 0
for idx_line, _line in enumerate(f):
line = _line.strip()
if len(line) > 0:
prefix, remainder = line[0], line[2:]
if prefix == "S":
src_sent = remainder.split(" ")
tgt_sent = deepcopy(src_sent)
elif prefix == "A":
annotation_data = remainder.split("|||")
idx_start, idx_end = map(int, annotation_data[0].split(" "))
edit_type, edit_text = annotation_data[1], annotation_data[2]
if edit_type in skip_edits:
continue
formatted_correction = {
"idx_src": list(range(idx_start, idx_end)),
"idx_tgt": [],
"corr_type": edit_type
}
annotator_id = int(annotation_data[-1])
assert annotator_id == 0
removal = len(edit_text) == 0 or edit_text == "-NONE-"
if removal:
for idx_to_remove in range(idx_start, idx_end):
del tgt_sent[offset + idx_to_remove]
offset -= 1
else: # replacement/insertion
edit_tokens = edit_text.split(" ")
len_diff = len(edit_tokens) - (idx_end - idx_start)
formatted_correction["idx_tgt"] = list(
range(offset + idx_start, offset + idx_end + len_diff))
tgt_sent[offset + idx_start: offset + idx_end] = edit_tokens
offset += len_diff
corrections.append(formatted_correction)
else: # empty line, indicating end of example
yield idx_ex, {
"src_tokens": src_sent,
"tgt_tokens": tgt_sent,
"corrections": corrections
}
src_sent, tgt_sent, corrections, offset = None, None, [], 0
idx_ex += 1
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