Datasets:
discovery

	---
	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-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## 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](https://github.com/sileod) for adding this dataset.