Update README.md

README.md

@@ -145,18 +145,21 @@ After you run the create workflow script, you can import the resulting `csv` fil
 
 Please cite Fool Me Twice: Entailment from Wikipedia Gamification published at NAACL 2021
 ```
-@inproceedings{eisenschlos-etal-2021-fm2,
-    title = "Fool Me Twice: Entailment from Wikipedia Gamification",
-    author = "Eisenschlos, Julian Martin and
-      Dhingra, Bhuwan and
-      Bulian, Jannis and
-      B{\"o}rschinger, Benjamin and
-      Boyd-Graber, Jordan",
-    booktitle = "Proceedings of the 2021 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
+@inproceedings{eisenschlos-etal-2021-fool,
+    title = "Fool Me Twice: Entailment from {W}ikipedia Gamification",
+    author = {Eisenschlos, Julian Martin  and
+      Dhingra, Bhuwan  and
+      Bulian, Jannis  and
+      B{\"o}rschinger, Benjamin  and
+      Boyd-Graber, Jordan},
+    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
+    month = jun,
     year = "2021",
+    address = "Online",
     publisher = "Association for Computational Linguistics",
-    url = "https://arxiv.org/abs/2104.04725",
-    abstract = "We release FoolMeTwice (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game. Gamification encourages adversarial examples, drastically lowering the number of examples that can be solved using shortcuts compared to other popular entailment datasets. Players are presented with two tasks. The first task asks the player to write a plausible claim based on the evidence from a Wikipedia page. The second one shows two plausible claims written by other players, one of which is false, and the goal is to identify it before the time runs out. Players pay to see clues retrieved from the evidence pool: the more evidence the player needs, the harder the claim. Game-play between motivated players leads to diverse strategies for crafting claims, such as temporal inference and diverting to unrelated evidence, and results in higher quality data for the entailment and evidence retrieval tasks. We open source the dataset and game code.",
+    url = "https://www.aclweb.org/anthology/2021.naacl-main.32",
+    pages = "352--365",
+    abstract = "We release FoolMeTwice (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game. Gamification encourages adversarial examples, drastically lowering the number of examples that can be solved using {``}shortcuts{''} compared to other popular entailment datasets. Players are presented with two tasks. The first task asks the player to write a plausible claim based on the evidence from a Wikipedia page. The second one shows two plausible claims written by other players, one of which is false, and the goal is to identify it before the time runs out. Players {``}pay{''} to see clues retrieved from the evidence pool: the more evidence the player needs, the harder the claim. Game-play between motivated players leads to diverse strategies for crafting claims, such as temporal inference and diverting to unrelated evidence, and results in higher quality data for the entailment and evidence retrieval tasks. We open source the dataset and the game code.",
 }
 ```