discovery / discovery.py
# coding=utf-8
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#
# Licensed under the Apache License, Version 2.0 (the "License");
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# limitations under the License.
# Lint as: python3
"""Discourse marker prediction with 174 different markers"""
import csv
import os
import textwrap
import datasets
_Discovery_CITATION = """@inproceedings{sileo-etal-2019-mining,
title = "Mining Discourse Markers for Unsupervised Sentence Representation Learning",
author = "Sileo, Damien and
Van De Cruys, Tim and
Pradel, Camille and
Muller, Philippe",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N19-1351",
pages = "3477--3486",
abstract = "Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct. Very little work adequately exploits unannotated data {--} such as discourse markers between sentences {--} mainly because of data sparseness and ineffective extraction methods. In the present work, we propose a method to automatically discover sentence pairs with relevant discourse markers, and apply it to massive amounts of data. Our resulting dataset contains 174 discourse markers with at least 10k examples each, even for rare markers such as {``}coincidentally{''} or {``}amazingly{''}. We use the resulting data as supervision for learning transferable sentence embeddings. In addition, we show that even though sentence representation learning through prediction of discourse marker yields state of the art results across different transfer tasks, it{'}s not clear that our models made use of the semantic relation between sentences, thus leaving room for further improvements.",
}
"""
_Discovery_DESCRIPTION = r"""\
Discourse marker prediction with 174 different markers
https://github.com/synapse-developpement/Discovery
"""
DATA_URL = "https://www.dropbox.com/s/aox84z90nyyuikz/discovery.zip?dl=1"
LABELS = [
"[no-conn]",
"absolutely,",
"accordingly",
"actually,",
"additionally",
"admittedly,",
"afterward",
"again,",
"already,",
"also,",
"alternately,",
"alternatively",
"although,",
"altogether,",
"amazingly,",
"and",
"anyway,",
"apparently,",
"arguably,",
"as_a_result,",
"basically,",
"because_of_that",
"because_of_this",
"besides,",
"but",
"by_comparison,",
"by_contrast,",
"by_doing_this,",
"by_then",
"certainly,",
"clearly,",
"coincidentally,",
"collectively,",
"consequently",
"conversely",
"curiously,",
"currently,",
"elsewhere,",
"especially,",
"essentially,",
"eventually,",
"evidently,",
"finally,",
"first,",
"firstly,",
"for_example",
"for_instance",
"fortunately,",
"frankly,",
"frequently,",
"further,",
"furthermore",
"generally,",
"gradually,",
"happily,",
"hence,",
"here,",
"historically,",
"honestly,",
"hopefully,",
"however",
"ideally,",
"immediately,",
"importantly,",
"in_contrast,",
"in_fact,",
"in_other_words",
"in_particular,",
"in_short,",
"in_sum,",
"in_the_end,",
"in_the_meantime,",
"in_turn,",
"incidentally,",
"increasingly,",
"indeed,",
"inevitably,",
"initially,",
"instead,",
"interestingly,",
"ironically,",
"lastly,",
"lately,",
"later,",
"likewise,",
"locally,",
"luckily,",
"maybe,",
"meaning,",
"meantime,",
"meanwhile,",
"moreover",
"mostly,",
"namely,",
"nationally,",
"naturally,",
"nevertheless",
"next,",
"nonetheless",
"normally,",
"notably,",
"now,",
"obviously,",
"occasionally,",
"oddly,",
"often,",
"on_the_contrary,",
"on_the_other_hand",
"once,",
"only,",
"optionally,",
"or,",
"originally,",
"otherwise,",
"overall,",
"particularly,",
"perhaps,",
"personally,",
"plus,",
"preferably,",
"presently,",
"presumably,",
"previously,",
"probably,",
"rather,",
"realistically,",
"really,",
"recently,",
"regardless,",
"remarkably,",
"sadly,",
"second,",
"secondly,",
"separately,",
"seriously,",
"significantly,",
"similarly,",
"simultaneously",
"slowly,",
"so,",
"sometimes,",
"soon,",
"specifically,",
"still,",
"strangely,",
"subsequently,",
"suddenly,",
"supposedly,",
"surely,",
"surprisingly,",
"technically,",
"thankfully,",
"then,",
"theoretically,",
"thereafter,",
"thereby,",
"therefore",
"third,",
"thirdly,",
"this,",
"though,",
"thus,",
"together,",
"traditionally,",
"truly,",
"truthfully,",
"typically,",
"ultimately,",
"undoubtedly,",
"unfortunately,",
"unsurprisingly,",
"usually,",
"well,",
"yet,",
]
class DiscoveryConfig(datasets.BuilderConfig):
"""BuilderConfig for Discovery."""
def __init__(
self,
text_features,
label_classes=None,
process_label=lambda x: x,
**kwargs,
):
"""BuilderConfig for Discovery.
Args:
text_features: `dict[string, string]`, map from the name of the feature
dict for each text field to the name of the column in the tsv file
label_column: `string`, name of the column in the tsv file corresponding
to the label
data_url: `string`, url to download the zip file from
data_dir: `string`, the path to the folder containing the tsv files in the
downloaded zip
citation: `string`, citation for the data set
url: `string`, url for information about the data set
label_classes: `list[string]`, the list of classes if the label is
categorical. If not provided, then the label will be of type
`datasets.Value('float32')`.
process_label: `Function[string, any]`, function taking in the raw value
of the label and processing it to the form required by the label feature
**kwargs: keyword arguments forwarded to super.
"""
super(DiscoveryConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.text_features = text_features
self.label_column = "label"
self.label_classes = LABELS
self.data_url = DATA_URL
self.data_dir = os.path.join("discovery", self.name)
self.citation = textwrap.dedent(_Discovery_CITATION)
self.process_label = process_label
self.description = ""
self.url = ""
class Discovery(datasets.GeneratorBasedBuilder):
"""Discourse marker prediction with 174 different markers"""
BUILDER_CONFIG_CLASS = DiscoveryConfig
BUILDER_CONFIGS = [
DiscoveryConfig(
name="discovery",
text_features={"sentence1": "sentence1", "sentence2": "sentence2"},
),
DiscoveryConfig(
name="discoverysmall",
text_features={"sentence1": "sentence1", "sentence2": "sentence2"},
),
]
def _info(self):
features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()}
if self.config.label_classes:
features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
else:
features["label"] = datasets.Value("float32")
features["idx"] = datasets.Value("int32")
return datasets.DatasetInfo(
description=_Discovery_DESCRIPTION,
features=datasets.Features(features),
homepage=self.config.url,
citation=self.config.citation + "\n" + _Discovery_CITATION,
)
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract(self.config.data_url)
data_dir = os.path.join(dl_dir, self.config.data_dir)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": os.path.join(data_dir or "", "train.tsv"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"data_file": os.path.join(data_dir or "", "dev.tsv"),
"split": "dev",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": os.path.join(data_dir or "", "test.tsv"),
"split": "test",
},
),
]
def _generate_examples(self, data_file, split):
process_label = self.config.process_label
label_classes = self.config.label_classes
with open(data_file, encoding="utf8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for n, row in enumerate(reader):
example = {feat: row[col] for feat, col in self.config.text_features.items()}
example["idx"] = n
if self.config.label_column in row:
label = row[self.config.label_column]
if label_classes and label not in label_classes:
label = int(label) if label else None
example["label"] = process_label(label)
else:
example["label"] = process_label(-1)
yield example["idx"], example