Update README.md

README.md

@@ -77,12 +77,11 @@ or available. The reader expects the specification of a language-specific config
 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
+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 our EMNLP paper, or our EMNLP slides for full details.
+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 of the TACRED dataset, which you should consider using instead of
+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.