--- 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 configs: - discovery - discoverysmall 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 --- # 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.