TajaKuzmanPungersek/X-GENRE-text-genre-dataset

Multilingual manually-annotated X-GENRE genre dataset

Multilingual (English-Slovenian) manually-annotated X-GENRE genre dataset is to be used for automatic genre identification, namely, for training genre classifiers (on the training split) and evaluation in the in-dataset scenario (on the test split).

The dataset was presented in the paper "Automatic Genre Identification for Robust Enrichment of Massive Text Collections: Investigation of Classification Methods in the Era of Large Language Models" (Kuzman et al., 2023).

To download the dataset as a HuggingFace dataset:

from datasets import load_dataset
import pandas as pd

train = load_dataset("TajaKuzman/X-GENRE-text-genre-dataset", "train")
test = load_dataset("TajaKuzman/X-GENRE-text-genre-dataset", "test")
dev = load_dataset("TajaKuzman/X-GENRE-text-genre-dataset", "dev")

# To open as Pandas DataFrame:
train_df = pd.DataFrame(train["train"])
dev_df = pd.DataFrame(dev["train"])
test_df = pd.DataFrame(test["train"])

The dataset can also be downloaded from the CLARIN.SI repository.

Dataset Summary

The X-GENRE dataset consists of almost 3,000 texts and 3.3 million words in English and Slovenian language. It is split into train, test and dev split. The dataset was used to develop the XLM-RoBERTa-based multilingual genre classifier X-GENRE, available on HuggingFace.

Dataset sizes:

split	# words	# texts
X-GENRE-train	1,940,317	1,772
X-GENRE-test	583,595	592
X-GENRE-dev	798,025	592
Total	3,321,937	2,956

The X-GENRE dataset was constructed by merging three manually-annotated datasets by mapping a joint X-GENRE genre schema to the original schemata:

• the Slovenian GINCO dataset (Kuzman et al., 2022),
• the English CORE dataset (Laippala et al., 2023)
• and the English FTD dataset (Sharoff, 2018).

Distribution of the original datasets in the X-GENRE dataset (in number of texts):

split	FTD	CORE	GINCO	Total
X-GENRE-train	630	607	535	1,772
X-GENRE-test	210	203	179	592
X-GENRE-dev	210	203	179	592
Total	1,050	1,013	893	2,956

Distribution of languages in the dataset (in number of texts):

split	Slovenian	English	Total
X-GENRE-train	535	1,237	1,772
X-GENRE-test	179	413	592
X-GENRE-dev	179	413	592
Total	893	2,063	2,956

Data Attributes

The dataset is in JSONL format. It has the following attributes:

• text: text instance
• labels: genre label
• dataset: original manually-annotated genre dataset from which the text was obtained (CORE, GINCO or FTD)
• language: language of the text (Slovenian or English)

Genre labels

The dataset uses the "X-GENRE" schema.

labels_list=['Other', 'Information/Explanation', 'News', 'Instruction', 'Opinion/Argumentation', 'Forum', 'Prose/Lyrical', 'Legal', 'Promotion']

Description of labels:

Label	Description	Examples
Information/Explanation	An objective text that describes or presents an event, a person, a thing, a concept etc. Its main purpose is to inform the reader about something. Common features: objective/factual, explanation/definition of a concept (x is …), enumeration.	research article, encyclopedia article, informational blog, product specification, course materials, general information, job description, manual, horoscope, travel guide, glossaries, historical article, biographical story/history.
Instruction	An objective text which instructs the readers on how to do something. Common features: multiple steps/actions, chronological order, 1st person plural or 2nd person, modality (must, have to, need to, can, etc.), adverbial clauses of manner (in a way that), of condition (if), of time (after …).	how-to texts, recipes, technical support
Legal	An objective formal text that contains legal terms and is clearly structured. The name of the text type is often included in the headline (contract, rules, amendment, general terms and conditions, etc.). Common features: objective/factual, legal terms, 3rd person.	small print, software license, proclamation, terms and conditions, contracts, law, copyright notices, university regulation
News	An objective or subjective text which reports on an event recent at the time of writing or coming in the near future. Common features: adverbs/adverbial clauses of time and/or place (dates, places), many proper nouns, direct or reported speech, past tense.	news report, sports report, travel blog, reportage, police report, announcement
Opinion/Argumentation	A subjective text in which the authors convey their opinion or narrate their experience. It includes promotion of an ideology and other non-commercial causes. This genre includes a subjective narration of a personal experience as well. Common features: adjectives/adverbs that convey opinion, words that convey (un)certainty (certainly, surely), 1st person, exclamation marks.	review, blog (personal blog, travel blog), editorial, advice, letter to editor, persuasive article or essay, formal speech, pamphlet, political propaganda, columns, political manifesto
Promotion	A subjective text intended to sell or promote an event, product, or service. It addresses the readers, often trying to convince them to participate in something or buy something. Common features: contains adjectives/adverbs that promote something (high-quality, perfect, amazing), comparative and superlative forms of adjectives and adverbs (the best, the greatest, the cheapest), addressing the reader (usage of 2nd person), exclamation marks.	advertisement, promotion of a product (e-shops), promotion of an accommodation, promotion of company's services, invitation to an event
Forum	A text in which people discuss a certain topic in form of comments. Common features: multiple authors, informal language, subjective (the writers express their opinions), written in 1st person.	discussion forum, reader/viewer responses, QA forum
Prose/Lyrical	A literary text that consists of paragraphs or verses. A literary text is deemed to have no other practical purpose than to give pleasure to the reader. Often the author pays attention to the aesthetic appearance of the text. It can be considered as art.	lyrics, poem, prayer, joke, novel, short story
Other	A text that which does not fall under any of other genre categories.

Citation information

Please cite the following paper which describes the construction of the dataset in more details:

@article{make5030059,
AUTHOR = {Kuzman, Taja and Mozetič, Igor and Ljubešić, Nikola},
TITLE = {Automatic Genre Identification for Robust Enrichment of Massive Text Collections: Investigation of Classification Methods in the Era of Large Language Models},
JOURNAL = {Machine Learning and Knowledge Extraction},
VOLUME = {5},
YEAR = {2023},
NUMBER = {3},
PAGES = {1149--1175},
URL = {https://www.mdpi.com/2504-4990/5/3/59},
ISSN = {2504-4990},
DOI = {10.3390/make5030059}
}