# 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