@@ -4,7 +4,8 @@ We introduce an attention mechanism that pinpoints relevant context not only for
The advantage of our neural model is that it avoids the need to reproduce linguistic features for other languages and is thus more easily transferable.
The advantage of our neural model is that it avoids the need to reproduce linguistic features for other languages and is thus more easily transferable.
We provide the code for the basic local model (GRU), the local model with attention (GRU+attention), and our best performing context model which uses labels of previous clauses and genre information (GRU+attention+label+genre).
We provide the code for the basic local model (GRU), the local model with attention (GRU+attention), and our best performing context model which uses labels of previous clauses and genre information (GRU+attention+label+genre).
The data we used for our experiments can be found here, and we used the same train-dev-test split: https://github.com/annefried/sitent/tree/master/annotated_corpus
References:
References:
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention.
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention.
