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albarron · May 18, 2020 · 8ff8b13 · 8ff8b13
1 parent 2fff6be
commit 8ff8b13
Showing 1 changed file with 10 additions and 11 deletions.
diff --git a/semeval20-task11.bib b/semeval20-task11.bib
@@ -8,17 +8,6 @@ @InProceedings{1stLastname
 crossref = "SemEval20"
 }
 
-@InProceedings{Martinkovic,
-author = "Martinkovic, Matej and
-  Pecar, Samuel and
-  Simko, Marian",
-title = "NLFIIT at {SemEval}-2020 Task 11: Neural Network Architectures for Detection of Propaganda Techniques in News Articles",
-pages = "",
-abstract = "Since propaganda became more common technique in news, it is very important to look for possibilities of its automatic detection. In this paper, we present neural model architecture submitted to the SemEval-2020 Task 11 competition: Detection of Propaganda Techniques in News Articles. We participated in both subtasks, propaganda span identification and propaganda technique classification. Our model uses recurrent Bi-LSTM layers and also takes advantage of self-attention mechanism. Our model managed to achieve score 0.405 F1 for subtask 1 and 0.553 F1 for subtask 2 resulting in 18th and 16th place in subtask 1 and subtask 2, respectively.",
-crossref = "SemEval20"
-}
-
-
 @InProceedings{Blaschke,
 author = "Blaschke, Verena and
   Korniyenko, Maxim and
@@ -68,6 +57,16 @@ @InProceedings{Kaas
 crossref = "SemEval20"
 }
 
+@InProceedings{Martinkovic,
+author = "Martinkovic, Matej and
+  Pecar, Samuel and
+  Simko, Marian",
+title = "NLFIIT at {SemEval}-2020 Task 11: Neural Network Architectures for Detection of Propaganda Techniques in News Articles",
+pages = "",
+abstract = "Since propaganda became more common technique in news, it is very important to look for possibilities of its automatic detection. In this paper, we present neural model architecture submitted to the SemEval-2020 Task 11 competition: Detection of Propaganda Techniques in News Articles. We participated in both subtasks, propaganda span identification and propaganda technique classification. Our model uses recurrent Bi-LSTM layers and also takes advantage of self-attention mechanism. Our model managed to achieve score 0.405 F1 for subtask 1 and 0.553 F1 for subtask 2 resulting in 18th and 16th place in subtask 1 and subtask 2, respectively.",
+crossref = "SemEval20"
+}
+
 @InProceedings{Paraschiv,
 author = "Paraschiv, Andrei and
   Cercel, Dumitru-Clementin",