Bilateral Multi-Perspective Matching for Natural Language Sentences
Table 3: Performance for natural language inference on the SNLI dataset.
['Models', 'Accuracy']
First, we can see that “Only P←Q” works significantly better than “Only P→Q”, which tells us that, for natural language inference, matching the hypothesis against the premise is more effective than the other way around. Second, our “BiMPM” model works much better than “Only P←Q”, which reveals that matching premise against the hypothesis can also bring some benefits. (Ensemble)”. Therefore, our models achieve the state-of-the-art performance in both single and ensemble scenarios for the natural language inference task.