Differentiable Representations For Multihop Inference Rules
Table 2: Left: Hits@1 for the WebQuestionsSP and MetaQA datasets. Results for KV-Mem and VRN on MetaQA are from zhang2017variational ; results for GRAFT-Net and KV-Mem on WebQSP are from graftnet . Systems marked with a star ∗ construct question-specific autographs, based on the KB, at inference time. Right: Hits@1 and hits@10 for two KB completion tasks. Starred KB completion methods are transductive, and do not generalize to entities not seen in training.
['[EMPTY]', 'WebQSP 1-2 hops', 'MetaQA 1-hop', 'MetaQA 2-hop', 'MetaQA 3-hop']
We use unregularized cross entropy loss. For this large KB, we split the KB across three 12-Gb GPUs, and used a fourth GPU for the rest of the model. End-to-end QA for longer reasoning chains. Following past work, we train and test our model with 1-hop, 2-hop or 3-hop questions separately. For each step of inference we construct a relation sets r. Let x0 be the set of entities associated with q. The full model computes is for t=1,2,3: rt= ft(q) ; x% t=follow(xt−1,rt) The ft’s are again learned linear projections of a pooled bag-of-words embedding, and again we compute the softmax of the appropriate set xk (where k is the number of hops associated with task) and use cross entropy loss. Discussion of QA results. baselines RSF consistently outperforms the baseline, dramatically so for longer reasoning chains. The synthetic task shows that there is very little degradation as chain length increases, with Hits@1 for 10 hops still 89.7%.