Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Language Creators:
other
Annotations Creators:
other
Source Datasets:
original
License:
metadata
annotations_creators:
- other
language_creators:
- other
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: discovery
pretty_name: Discovery
tags:
- discourse-marker-prediction
dataset_info:
- config_name: discovery
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': '[no-conn]'
'1': absolutely,
'2': accordingly
'3': actually,
'4': additionally
'5': admittedly,
'6': afterward
'7': again,
'8': already,
'9': also,
'10': alternately,
'11': alternatively
'12': although,
'13': altogether,
'14': amazingly,
'15': and
'16': anyway,
'17': apparently,
'18': arguably,
'19': as_a_result,
'20': basically,
'21': because_of_that
'22': because_of_this
'23': besides,
'24': but
'25': by_comparison,
'26': by_contrast,
'27': by_doing_this,
'28': by_then
'29': certainly,
'30': clearly,
'31': coincidentally,
'32': collectively,
'33': consequently
'34': conversely
'35': curiously,
'36': currently,
'37': elsewhere,
'38': especially,
'39': essentially,
'40': eventually,
'41': evidently,
'42': finally,
'43': first,
'44': firstly,
'45': for_example
'46': for_instance
'47': fortunately,
'48': frankly,
'49': frequently,
'50': further,
'51': furthermore
'52': generally,
'53': gradually,
'54': happily,
'55': hence,
'56': here,
'57': historically,
'58': honestly,
'59': hopefully,
'60': however
'61': ideally,
'62': immediately,
'63': importantly,
'64': in_contrast,
'65': in_fact,
'66': in_other_words
'67': in_particular,
'68': in_short,
'69': in_sum,
'70': in_the_end,
'71': in_the_meantime,
'72': in_turn,
'73': incidentally,
'74': increasingly,
'75': indeed,
'76': inevitably,
'77': initially,
'78': instead,
'79': interestingly,
'80': ironically,
'81': lastly,
'82': lately,
'83': later,
'84': likewise,
'85': locally,
'86': luckily,
'87': maybe,
'88': meaning,
'89': meantime,
'90': meanwhile,
'91': moreover
'92': mostly,
'93': namely,
'94': nationally,
'95': naturally,
'96': nevertheless
'97': next,
'98': nonetheless
'99': normally,
'100': notably,
'101': now,
'102': obviously,
'103': occasionally,
'104': oddly,
'105': often,
'106': on_the_contrary,
'107': on_the_other_hand
'108': once,
'109': only,
'110': optionally,
'111': or,
'112': originally,
'113': otherwise,
'114': overall,
'115': particularly,
'116': perhaps,
'117': personally,
'118': plus,
'119': preferably,
'120': presently,
'121': presumably,
'122': previously,
'123': probably,
'124': rather,
'125': realistically,
'126': really,
'127': recently,
'128': regardless,
'129': remarkably,
'130': sadly,
'131': second,
'132': secondly,
'133': separately,
'134': seriously,
'135': significantly,
'136': similarly,
'137': simultaneously
'138': slowly,
'139': so,
'140': sometimes,
'141': soon,
'142': specifically,
'143': still,
'144': strangely,
'145': subsequently,
'146': suddenly,
'147': supposedly,
'148': surely,
'149': surprisingly,
'150': technically,
'151': thankfully,
'152': then,
'153': theoretically,
'154': thereafter,
'155': thereby,
'156': therefore
'157': third,
'158': thirdly,
'159': this,
'160': though,
'161': thus,
'162': together,
'163': traditionally,
'164': truly,
'165': truthfully,
'166': typically,
'167': ultimately,
'168': undoubtedly,
'169': unfortunately,
'170': unsurprisingly,
'171': usually,
'172': well,
'173': yet,
- name: idx
dtype: int32
splits:
- name: train
num_bytes: 334809726
num_examples: 1566000
- name: validation
num_bytes: 18607661
num_examples: 87000
- name: test
num_bytes: 18615474
num_examples: 87000
download_size: 146233621
dataset_size: 372032861
- config_name: discoverysmall
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': '[no-conn]'
'1': absolutely,
'2': accordingly
'3': actually,
'4': additionally
'5': admittedly,
'6': afterward
'7': again,
'8': already,
'9': also,
'10': alternately,
'11': alternatively
'12': although,
'13': altogether,
'14': amazingly,
'15': and
'16': anyway,
'17': apparently,
'18': arguably,
'19': as_a_result,
'20': basically,
'21': because_of_that
'22': because_of_this
'23': besides,
'24': but
'25': by_comparison,
'26': by_contrast,
'27': by_doing_this,
'28': by_then
'29': certainly,
'30': clearly,
'31': coincidentally,
'32': collectively,
'33': consequently
'34': conversely
'35': curiously,
'36': currently,
'37': elsewhere,
'38': especially,
'39': essentially,
'40': eventually,
'41': evidently,
'42': finally,
'43': first,
'44': firstly,
'45': for_example
'46': for_instance
'47': fortunately,
'48': frankly,
'49': frequently,
'50': further,
'51': furthermore
'52': generally,
'53': gradually,
'54': happily,
'55': hence,
'56': here,
'57': historically,
'58': honestly,
'59': hopefully,
'60': however
'61': ideally,
'62': immediately,
'63': importantly,
'64': in_contrast,
'65': in_fact,
'66': in_other_words
'67': in_particular,
'68': in_short,
'69': in_sum,
'70': in_the_end,
'71': in_the_meantime,
'72': in_turn,
'73': incidentally,
'74': increasingly,
'75': indeed,
'76': inevitably,
'77': initially,
'78': instead,
'79': interestingly,
'80': ironically,
'81': lastly,
'82': lately,
'83': later,
'84': likewise,
'85': locally,
'86': luckily,
'87': maybe,
'88': meaning,
'89': meantime,
'90': meanwhile,
'91': moreover
'92': mostly,
'93': namely,
'94': nationally,
'95': naturally,
'96': nevertheless
'97': next,
'98': nonetheless
'99': normally,
'100': notably,
'101': now,
'102': obviously,
'103': occasionally,
'104': oddly,
'105': often,
'106': on_the_contrary,
'107': on_the_other_hand
'108': once,
'109': only,
'110': optionally,
'111': or,
'112': originally,
'113': otherwise,
'114': overall,
'115': particularly,
'116': perhaps,
'117': personally,
'118': plus,
'119': preferably,
'120': presently,
'121': presumably,
'122': previously,
'123': probably,
'124': rather,
'125': realistically,
'126': really,
'127': recently,
'128': regardless,
'129': remarkably,
'130': sadly,
'131': second,
'132': secondly,
'133': separately,
'134': seriously,
'135': significantly,
'136': similarly,
'137': simultaneously
'138': slowly,
'139': so,
'140': sometimes,
'141': soon,
'142': specifically,
'143': still,
'144': strangely,
'145': subsequently,
'146': suddenly,
'147': supposedly,
'148': surely,
'149': surprisingly,
'150': technically,
'151': thankfully,
'152': then,
'153': theoretically,
'154': thereafter,
'155': thereby,
'156': therefore
'157': third,
'158': thirdly,
'159': this,
'160': though,
'161': thus,
'162': together,
'163': traditionally,
'164': truly,
'165': truthfully,
'166': typically,
'167': ultimately,
'168': undoubtedly,
'169': unfortunately,
'170': unsurprisingly,
'171': usually,
'172': well,
'173': yet,
- name: idx
dtype: int32
splits:
- name: train
num_bytes: 3355192
num_examples: 15662
- name: validation
num_bytes: 185296
num_examples: 871
- name: test
num_bytes: 187471
num_examples: 869
download_size: 146233621
dataset_size: 3727959
train-eval-index:
- config: discovery
task: text-classification
task_id: multi-class-classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
- config: discoverysmall
task: text-classification
task_id: multi-class-classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
config_names:
- discovery
- discoverysmall
Dataset Card for Discovery
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/sileod/Discovery
- Repository: https://github.com/sileod/Discovery
- Paper: https://www.aclweb.org/anthology/N19-1351/
- Leaderboard:
- Point of Contact: damien.sileo at inria.fr
Dataset Summary
Discourse marker prediction with 174 markers
Supported Tasks and Leaderboards
[More Information Needed]
Languages
English
Dataset Structure
input : sentence1, sentence2, label: marker originally between sentence1 and sentence2
Data Instances
[More Information Needed]
Data Fields
[More Information Needed]
Data Splits
Train/Val/Test
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Aranea english web corpus
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Self supervised (see paper)
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
@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.",
}
Contributions
Thanks to @sileod for adding this dataset.