Datasets:
tweets_ar_en_parallel

- {"parallelTweets": {"description": " Twitter users often post parallel tweets\u2014tweets that contain the same content but are\n written in different languages. Parallel tweets can be an important resource for developing\n machine translation (MT) systems among other natural language processing (NLP) tasks. This\n resource is a result of a generic method for collecting parallel tweets. Using the method,\n we compiled a bilingual corpus of English-Arabic parallel tweets and a list of Twitter accounts\n who post English-Arabic tweets regularly. Additionally, we annotate a subset of Twitter accounts\n with their countries of origin and topic of interest, which provides insights about the population\n who post parallel tweets.\n", "citation": " @inproceedings{Mubarak2020bilingualtweets,\ntitle={Constructing a Bilingual Corpus of Parallel Tweets},\nauthor={Mubarak, Hamdy and Hassan, Sabit and Abdelali, Ahmed},\nbooktitle={Proceedings of 13th Workshop on Building and Using Comparable Corpora (BUCC)},\naddress={Marseille, France},\nyear={2020}\n}\n", "homepage": "https://alt.qcri.org/resources/bilingual_corpus_of_parallel_tweets", "license": "", "features": {"ArabicTweetID": {"dtype": "int64", "id": null, "_type": "Value"}, "EnglishTweetID": {"dtype": "int64", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "tweets_ar_en_parallel", "config_name": "parallelTweets", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 2667296, "num_examples": 166706, "dataset_name": "tweets_ar_en_parallel"}}, "download_checksums": {"https://alt.qcri.org/wp-content/uploads/2020/08/Bilingual-Corpus-of-Arabic-English-Parallel-Tweets.zip": {"num_bytes": 2937626, "checksum": "a2a20772745825c2e0180699083517128519975d85ec30f451aa5450209996e4"}}, "download_size": 2937626, "post_processing_size": null, "dataset_size": 2667296, "size_in_bytes": 5604922}, "accountList": {"description": " Twitter users often post parallel tweets\u2014tweets that contain the same content but are\n written in different languages. Parallel tweets can be an important resource for developing\n machine translation (MT) systems among other natural language processing (NLP) tasks. This\n resource is a result of a generic method for collecting parallel tweets. Using the method,\n we compiled a bilingual corpus of English-Arabic parallel tweets and a list of Twitter accounts\n who post English-Arabic tweets regularly. Additionally, we annotate a subset of Twitter accounts\n with their countries of origin and topic of interest, which provides insights about the population\n who post parallel tweets.\n", "citation": " @inproceedings{Mubarak2020bilingualtweets,\ntitle={Constructing a Bilingual Corpus of Parallel Tweets},\nauthor={Mubarak, Hamdy and Hassan, Sabit and Abdelali, Ahmed},\nbooktitle={Proceedings of 13th Workshop on Building and Using Comparable Corpora (BUCC)},\naddress={Marseille, France},\nyear={2020}\n}\n", "homepage": "https://alt.qcri.org/resources/bilingual_corpus_of_parallel_tweets", "license": "", "features": {"account": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "tweets_ar_en_parallel", "config_name": "accountList", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 20108, "num_examples": 1389, "dataset_name": "tweets_ar_en_parallel"}}, "download_checksums": {"https://alt.qcri.org/wp-content/uploads/2020/08/Bilingual-Corpus-of-Arabic-English-Parallel-Tweets.zip": {"num_bytes": 2937626, "checksum": "a2a20772745825c2e0180699083517128519975d85ec30f451aa5450209996e4"}}, "download_size": 2937626, "post_processing_size": null, "dataset_size": 20108, "size_in_bytes": 2957734}, "countryTopicAnnotation": {"description": " Twitter users often post parallel tweets\u2014tweets that contain the same content but are\n written in different languages. Parallel tweets can be an important resource for developing\n machine translation (MT) systems among other natural language processing (NLP) tasks. This\n resource is a result of a generic method for collecting parallel tweets. Using the method,\n we compiled a bilingual corpus of English-Arabic parallel tweets and a list of Twitter accounts\n who post English-Arabic tweets regularly. Additionally, we annotate a subset of Twitter accounts\n with their countries of origin and topic of interest, which provides insights about the population\n who post parallel tweets.\n", "citation": " @inproceedings{Mubarak2020bilingualtweets,\ntitle={Constructing a Bilingual Corpus of Parallel Tweets},\nauthor={Mubarak, Hamdy and Hassan, Sabit and Abdelali, Ahmed},\nbooktitle={Proceedings of 13th Workshop on Building and Using Comparable Corpora (BUCC)},\naddress={Marseille, France},\nyear={2020}\n}\n", "homepage": "https://alt.qcri.org/resources/bilingual_corpus_of_parallel_tweets", "license": "", "features": {"account": {"dtype": "string", "id": null, "_type": "Value"}, "country": {"num_classes": 12, "names": ["QA", "BH", "AE", "OM", "SA", "PL", "JO", "IQ", "Other", "EG", "KW", "SY"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 12, "names": ["Gov", "Culture", "Education", "Sports", "Travel", "Events", "Business", "Science", "Politics", "Health", "Governoment", "Media"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "tweets_ar_en_parallel", "config_name": "countryTopicAnnotation", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 6036, "num_examples": 200, "dataset_name": "tweets_ar_en_parallel"}}, "download_checksums": {"https://alt.qcri.org/wp-content/uploads/2020/08/Bilingual-Corpus-of-Arabic-English-Parallel-Tweets.zip": {"num_bytes": 2937626, "checksum": "a2a20772745825c2e0180699083517128519975d85ec30f451aa5450209996e4"}}, "download_size": 2937626, "post_processing_size": null, "dataset_size": 6036, "size_in_bytes": 2943662}}