[HTML][HTML] Conversational question answering: A survey
Question answering (QA) systems provide a way of querying the information available in
various formats including, but not limited to, unstructured and structured data in natural …
various formats including, but not limited to, unstructured and structured data in natural …
Deep bidirectional language-knowledge graph pretraining
Pretraining a language model (LM) on text has been shown to help various downstream
NLP tasks. Recent works show that a knowledge graph (KG) can complement text data …
NLP tasks. Recent works show that a knowledge graph (KG) can complement text data …
QA-GNN: Reasoning with language models and knowledge graphs for question answering
The problem of answering questions using knowledge from pre-trained language models
(LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question …
(LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question …
Generated knowledge prompting for commonsense reasoning
It remains an open question whether incorporating external knowledge benefits
commonsense reasoning while maintaining the flexibility of pretrained sequence models. To …
commonsense reasoning while maintaining the flexibility of pretrained sequence models. To …
Greaselm: Graph reasoning enhanced language models for question answering
Answering complex questions about textual narratives requires reasoning over both stated
context and the world knowledge that underlies it. However, pretrained language models …
context and the world knowledge that underlies it. However, pretrained language models …
Krisp: Integrating implicit and symbolic knowledge for open-domain knowledge-based vqa
One of the most challenging question types in VQA is when answering the question requires
outside knowledge not present in the image. In this work we study open-domain knowledge …
outside knowledge not present in the image. In this work we study open-domain knowledge …
CommonGen: A constrained text generation challenge for generative commonsense reasoning
Recently, large-scale pre-trained language models have demonstrated impressive
performance on several commonsense-reasoning benchmark datasets. However, building …
performance on several commonsense-reasoning benchmark datasets. However, building …
Graph neural prompting with large language models
Large language models (LLMs) have shown remarkable generalization capability with
exceptional performance in various language modeling tasks. However, they still exhibit …
exceptional performance in various language modeling tasks. However, they still exhibit …
[HTML][HTML] An overview of knowledge graph reasoning: key technologies and applications
In recent years, with the rapid development of Internet technology and applications, the
scale of Internet data has exploded, which contains a significant amount of valuable …
scale of Internet data has exploded, which contains a significant amount of valuable …
Scalable multi-hop relational reasoning for knowledge-aware question answering
Existing work on augmenting question answering (QA) models with external knowledge (eg,
knowledge graphs) either struggle to model multi-hop relations efficiently, or lack …
knowledge graphs) either struggle to model multi-hop relations efficiently, or lack …