Dirk Weissenborn

Making Neural QA as Simple as Possible but not Simpler

2017

Recent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-toend neural architectures for QA. Increasingly complex systems have been conceived without comparison to simpler neural baseline systems that would …

Dynamic Integration of Background Knowledge in Neural NLU Systems

2017 · arXiv (Cornell University)

Common-sense and background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, this knowledge must be acquired from training corpora during learning, and then it is static at …