Update multitacred.py

multitacred.py

@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2023 The current dataset script contributor.
+# Copyright 2022 The current dataset script contributor.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -81,32 +81,36 @@ _CITATION = """\
 """
 
 _DESCRIPTION = """\
-MultiTACRED is a multilingual version of the large-scale TAC Relation Extraction Dataset (LDC2018T24). 
-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 LDC2018T24. 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).
+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 our ACL paper for full details.
+ 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 default config is 'original-de'.
+(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()):
+_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 target
-language and then back to English). To access this split, access dataset['backtranslated_test'].
+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).
+
 """
 
 _HOMEPAGE = "https://github.com/DFKI-NLP/MultiTACRED"