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Maximum Margin Reward Networks for Learning from Explicit and Implicit Supervision
AbstractNeural networks have achieved state-of-the-art performance on several structured-output prediction tasks, trained in a fully supervised fashion. However, annotated examples in structured domains are often costly to obtain, which thus …...
Adaptable and Interpretable Neural MemoryOver Symbolic Knowledge
AbstractPast research has demonstrated that large neural language models (LMs) encode surprising amounts of factual information: however, augmenting or modifying this information requires modifying a corpus and retraining, which is …...
Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection
AbstractEnd-to-end question answering using a differentiable knowledge graph is a promising technique that requires only weak supervision, produces interpretable results, and is fully differentiable. Previous implementations of this technique (Cohen …...
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AbstractQuestion answering over knowledge bases (KBQA) for complex questions is a challenging task in natural language processing. Recently, generation-based methods that translate natural language questions to executable logical forms have …...
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases
AbstractComplex question generation over knowledge bases (KB) aims to generate natural language questions involving multiple KB relations or functional constraints. Existing methods train one encoder-decoder-based model to fit all questions. …...
Semantic Structure Based Query Graph Prediction for Question Answering over Knowledge Graph
AbstractBuilding query graphs from natural language questions is an important step in complex question answering over knowledge graph (Complex KGQA). In general, a question can be correctly answered if its …...
TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Base
AbstractPre-trained language models (PLMs) have shown their effectiveness in multiple scenarios. However, KBQA remains challenging, especially regarding coverage and generalization settings. This is due to two main factors: i) understanding …...