Ответы на вопросы на основе базы знаний

task ID: knowledgebasequestionanswering

Related Papers

Improved Neural Relation Detection for Knowledge Base Question Answering

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractRelation detection is a core component of many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning …...

40061

Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractThe most approaches to Knowledge Base Question Answering are based on semantic parsing. In this paper, we address the problem of learning vector representations for complex semantic parses that consist …...

5241

Leveraging Abstract Meaning Representation for Knowledge Base Question Answering

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

Meaning Representation for Knowledge Base Question Answering Pavan Kapanipathi∗, Ibrahim Abdelaziz∗, Srinivas Ravishankar∗, Salim Roukos, Alexander Gray, Ramon Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio …...

33846

A Sequential Flow Control Framework for Multi-hop Knowledge Base Question Answering

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractOne of the key challenges of knowledge base question answering (KBQA) is the multi-hop reasoning. Since in different hops, one attends to different parts of question, it is important to …...

30302

Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractRecent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning. The desired subgraph is crucial as a small one may exclude the answer but a large one …...

38986

Don’t Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractA key missing capacity of current language models (LMs) is grounding to real-world environments. Most existing work for grounded language understanding uses LMs to directly generate plans that can be …...

38586