Datasets:

DFKI-SLT
/

multitacred

@@ -4773,36 +4773,38 @@ dataset_info:
 - **Size of the generated dataset:** 40.9 MB
 - **Total amount of disk used:** 103.2 MB
 ### Dataset Summary
-TODO initial dataset reader for MultiTACRED, needs to be tested / finalized with dataset. Should use the the TACRED
-dataset reader (https://huggingface.co/datasets/DFKI-SLT/tacred) code but supply other download URLs per language,
-with language = config item. e.g. 'en-original', 'de-revised', 'ar-retacred' etc.
 NOTE: This Datasetreader supports a reduced version of the original TACRED JSON format with the following changes:
 - Removed fields: stanford_pos, stanford_ner, stanford_head, stanford_deprel, docid
 The motivation for this is that we want to support additional languages, for which these fields were not required
 or available. The reader expects the specification of a language-specific configuration specifying the variant
-(original or revised) and the language (as a two-letter iso code). The default config is 'original-en'.
-You can find the TACRED dataset reader for the original version of the
-dataset [here](https://huggingface.co/datasets/DFKI-SLT/tacred).
-The TAC Relation Extraction Dataset (TACRED) is a large-scale relation extraction dataset with 106,264 examples built over newswire and web text from the corpus used in the yearly TAC Knowledge Base Population (TAC KBP) challenges. Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended
-and org:members) or are labeled as no_relation if no defined relation is held. These examples are created by combining available human annotations from the TAC
-KBP challenges and crowdsourcing. Please see [Stanford's EMNLP paper](https://nlp.stanford.edu/pubs/zhang2017tacred.pdf), or their [EMNLP slides](https://nlp.stanford.edu/projects/tacred/files/position-emnlp2017.pdf) for full details.
-Note: There is currently a [label-corrected version](https://github.com/DFKI-NLP/tacrev) of the TACRED dataset, which you should consider using instead of
-the original version released in 2017. For more details on this new version, see the [TACRED Revisited paper](https://aclanthology.org/2020.acl-main.142/)
-published at ACL 2020.
 ### Supported Tasks and Leaderboards
 - **Tasks:** Relation Classification
-- **Leaderboards:** [https://paperswithcode.com/sota/relation-extraction-on-tacred](https://paperswithcode.com/sota/relation-extraction-on-tacred)
 ### Languages
 The languages in the dataset are Arabic, German, English, Spanish, Finnish, French, Hindi, Hungarian, Japanese, Polish, Russian, Turkish, and Chinese.
@@ -4816,14 +4818,14 @@ All languages except English are machine-translated using either Deepl's or Goog
 An example of 'train' looks as follows:
 ```json
 {
-  "id": "61b3a5c8c9a882dcfcd2",
-  "relation": "org:founded_by",
-  "token": ["Tom", "Thabane", "resigned", "in", "October", "last", "year", "to", "form", "the", "All", "Basotho", "Convention", "-LRB-", "ABC", "-RRB-", ",", "crossing", "the", "floor", "with", "17", "members", "of", "parliament", ",", "causing", "constitutional", "monarch", "King", "Letsie", "III", "to", "dissolve", "parliament", "and", "call", "the", "snap", "election", "."],
-  "subj_start": 10,
-  "subj_end": 13,
-  "obj_start": 0,
-  "obj_end": 2,
-  "subj_type": "ORGANIZATION",
   "obj_type": "PERSON"
 }
 ```
@@ -4844,21 +4846,21 @@ To miminize dataset bias, TACRED is stratified across years in which the TAC KBP
 Languages statistics for the splits differ because not all instances could be translated with the
 subject and object entity markup still intact, these were discarded.
-|    Language (Translation Engine - D = Deepl, G = Google)       | Train  |  Dev  | Test |
-| -----     | ------ | ----- | ---- |
-| English (en)    | 68,124 (TAC KBP 2009-2012) | 22,631 (TAC KBP 2013) | 15,509 (TAC KBP 2014) |
-| ar (G)   |  67,736 |  22,502 |  15,425 |
-| de (D)   |  67,205 |  22,343 |  15,282 |
-| es (D)   |  65,247 |  21,697 |  14,908 |
-| fi (D)   |  66,751 |  22,268 |  15,083 |
-| fr (D)   |  66,856 |  22,248 |  15,237 |
-| hi (G)   |  67,751 |  22,511 |  15,440 |
-| hu (G)   |  67,766 |  22,519 |  15,436 |
-| ja (D)   |  61,571 |  20,290 |  13,701 |
-| pl (G)   |  68,124 |  22,631 |  15,509 |
-| ru (D)   |  66,413 |  21,998 |  14,995 |
-| tr (G)   |  67,652 |  22,510 |  15,429 |
-| zh (D)   |  65,211 |  21,490 |  14,694 |
 ## Dataset Creation
 ### Curation Rationale
@@ -4923,8 +4925,6 @@ For the revised version, please also cite:
     doi = "10.18653/v1/2020.acl-main.142",
     pages = "1558--1569",
 }
 ```
 For the Re-TACRED version, please also cite:
@@ -4946,4 +4946,4 @@ For the Re-TACRED version, please also cite:
 }
 ```
 ### Contributions
-Thanks to [@leonhardhennig](https://github.com/leonhardhennig) for adding this dataset.

 - **Size of the generated dataset:** 40.9 MB
 - **Total amount of disk used:** 103.2 MB
 ### Dataset Summary
+MultiTACRED is a multilingual version of the large-scale [https://nlp.stanford.edu/projects/tacred/](TAC Relation
+Extraction Dataset). It covers 12 typologically diverse languages from 9 language families, and was created by the
+Speech & Language Technology group of DFKI by machine-translating the instances of the original TACRED dataset and
+automatically projecting their entity annotations. For details of the original TACRED's data collection and
+annotation process, see the [https://aclanthology.org/D17-1004/](original paper). Translations are syntactically
+validated by checking the correctness of the XML tag markup. Any translations with an invalid tag structure, e.g.
+missing or invalid head or tail tag pairs, are discarded (on average, 2.3% of the instances).
+Languages covered are: Arabic, Chinese, Finnish, French, German, Hindi, Hungarian, Japanese, Polish,
+ Russian, Spanish, Turkish. Intended use is supervised relation classification. Audience - researchers.
+ Please see [https://arxiv.org/abs/2305.04582](our ACL paper) for full details.
 NOTE: This Datasetreader supports a reduced version of the original TACRED JSON format with the following changes:
 - Removed fields: stanford_pos, stanford_ner, stanford_head, stanford_deprel, docid
 The motivation for this is that we want to support additional languages, for which these fields were not required
 or available. The reader expects the specification of a language-specific configuration specifying the variant
+(original, revisited or retacred) and the language (as a two-letter iso code).
+The DatasetReader changes the offsets of the following fields, to conform with standard Python usage (see
+_generate_examples()):
+- subj_end to subj_end + 1 (make end offset exclusive)
+- obj_end to obj_end + 1 (make end offset exclusive)
+NOTE 2: The MultiTACRED dataset offers an additional 'split', namely the backtranslated test data (translated to a
+target language and then back to English). To access this split, use dataset['backtranslated_test'].
+You can find the TACRED dataset reader for the English version of the dataset [here](https://huggingface.co/datasets/DFKI-SLT/tacred).
 ### Supported Tasks and Leaderboards
 - **Tasks:** Relation Classification
+- **Leaderboards:** [https://paperswithcode.com/sota/relation-extraction-on-multitacred](https://paperswithcode.com/sota/relation-extraction-on-multitacred)
 ### Languages
 The languages in the dataset are Arabic, German, English, Spanish, Finnish, French, Hindi, Hungarian, Japanese, Polish, Russian, Turkish, and Chinese.
 An example of 'train' looks as follows:
 ```json
 {
+  "id": "61b3a5c8c9a882dcfcd2",
+  "token": ["Tom", "Thabane", "trat", "im", "Oktober", "letzten", "Jahres", "zurück", ",", "um", "die", "All", "Basotho", "Convention", "-LRB-", "ABC", "-RRB-", "zu", "gründen", ",", "die", "mit", "17", "Abgeordneten", "das", "Wort", "ergriff", ",", "woraufhin", "der", "konstitutionelle", "Monarch", "König", "Letsie", "III.", "das", "Parlament", "auflöste", "und", "Neuwahlen", "ansetzte", "."],
+  "relation": "org:founded_by",
+  "subj_start": 11,
+  "subj_end": 13,
+  "obj_start": 0,
+  "obj_end": 1,
+  "subj_type": "ORGANIZATION",
   "obj_type": "PERSON"
 }
 ```
 Languages statistics for the splits differ because not all instances could be translated with the
 subject and object entity markup still intact, these were discarded.
+|    Language  | Train  |  Dev  | Test | Backtranslated Test | Translation Engine |
+| ----- | ------ | ----- | ---- | ---- | ---- |
+| en | 68,124 | 22,631 | 15,509 | - | - |
+| ar | 67,736 | 22,502 | 15,425 | 15,425 | Google |
+| de | 67,253 | 22,343 | 15,282 | 15,079 | DeepL |
+| es | 65,247 | 21,697 | 14,908 | 14,688 | DeepL |
+| fi | 66,751 | 22,268 | 15,083 | 14,462 | DeepL |
+| fr | 66,856 | 22,298 | 15,237 | 15,088 | DeepL |
+| hi | 67,751 | 22,511 | 15,440 | 15,440 | Google |
+| hu | 67,766 | 22,519 | 15,436 | 15,436 | Google |
+| ja | 61,571 | 20,290 | 13,701 | 12,913 | DeepL |
+| pl | 68,124 | 22,631 | 15,509 | 15,509 | Google |
+| ru | 66,413 | 21,998 | 14,995 | 14,703 | DeepL |
+| tr | 67,749 | 22,510 | 15,429 | 15,429 | Google |
+| zh | 65,260 | 21,538 | 14,694 | 14,021 | DeepL |
 ## Dataset Creation
 ### Curation Rationale
     doi = "10.18653/v1/2020.acl-main.142",
     pages = "1558--1569",
 }
 ```
 For the Re-TACRED version, please also cite:
 }
 ```
 ### Contributions
+Thanks to [@leonhardhennig](https://github.com/leonhardhennig) for adding this dataset.