File size: 10,572 Bytes
7a6289f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
af75501
 
7a6289f
 
 
 
 
 
 
 
 
 
 
 
 
4ec4542
7a6289f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4ec4542
7a6289f
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# 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
    
    DEFAULT_CONFIG_NAME = "discovery"

    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