QuASE: Question-Answer Driven Sentence Encoding
Table 6: The results of five variants of s-QuASE{}_{\small QAMR} on the development set of QAMR. We use the average exact match (EM) and average F1 as our evaluation metrics.
['Models', 'Model I', 'Model II', 'Model III', 'Model IV', 'Model V']
s-QuASE consists of three basic components: a sentence encoder for the sentence representation, a question encoder for the question representation, an interaction layer between the sentence component and the question component. We carefully designed five variants of s-QuASE with increasing complexity and performance: (I) Basic model: a fixed BERT and one-layer bidirectional transformer for sentence modeling, a fixed BERT and one-layer bidirectional transformer for question modeling, and a two-layer multi-layer perceptron (MLP) for the interaction layer; (II) a fine-tuned BERT; (III) the same as model II, with a bi-directional attention flow added to the question component; (IV) the same as model III, with the interaction layer changed from a two-layer MLP to a bidirectional transformer; (V) the same as model IV, with the sentence modeling layer and question modeling layer changed from a single-layer bi-directional transformer to a two-layer one, and beam search is used in the inference stage.