Antecedent Prediction Without a Pipeline.
CORBON@HLT-NAACL(2016)
摘要
We consider several antecedent prediction models that use no pipelined features generated by upstream systems. Models trained in this way are interesting because they allow for side-stepping the intricacies of upstream models, and because we might expect them to generalize better to situations in which upstream features are unavailable or unreliable. Through quantitative and qualitative error analysis we identify what sorts of cases are particularly difficult for such models, and suggest some directions for further improvement.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络