Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension
Table 1: Results on the NarrativeQA Test set. Models with † are generative, while the rest use span prediction.
['Model', 'B-1', 'R-L']
In this section we describe the experiments and results of our proposed model in different configurations. We report results for Bleu-1, and Rouge-L scores. The first section lists results on the NarrativeQA dataset as reported in NarrativeQADeepMind2017. Oracle (original) uses the gold answers as queries to match a token sequence (with the answer length) in the context that has the highest Rouge-L. This work). The Oracle performance in this setting is important since the produced annotations are used for training of the span-prediction systems, and is considered upper-bound. ASR is a version of the Attention Sum Reader Kadlec2016-as-reader implemented as a pointer-generator that reads the question and points to words in the context that are contained in the answer. BiDAF is Bi-Directional Attention Flow Seo2017-bidaf trained either with the Oracle (original) or Oracle (ours). Here, BiDAF and QANet are implementations available in the AllenNLP framework Gardner2017AllenNLP. In the last two rows we give the results of QANet extended with the proposed Discourse-Aware Semantic Self-Attention, using intra-sentential, Explicit discourse relations (DR (Exp), EMA is Exponential Moving Average).