An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks
Table 5: Comparison of input embeddings: the addition of character (char) and letter trigram (3gram) embeddings to word embeddings (word) clearly improve results on SQuAD and MS MARCO development sets.
['System', 'SQuAD', 'MS MARCO']
We observe marked improvements of update number +1.1 F1 in SQuAD and +0.9 ROUGE in MS MARCO. Although these improvements are not as large as those we achieved with multiple-turn reasoning, they are are still considerable and imply that robust representations of words is an important building block to strong RC models.