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 …
Representation learning for knowledge fusion and reasoning in Cyber–Physical–Social Systems: Survey and perspectives
The digital deep integration of cyber space, physical space and social space facilitates the
formation of Cyber–Physical–Social Systems (CPSS). Knowledge empowers CPSS to be …
formation of Cyber–Physical–Social Systems (CPSS). Knowledge empowers CPSS to be …
Scienceworld: Is your agent smarter than a 5th grader?
We present ScienceWorld, a benchmark to test agents' scientific reasoning abilities in a new
interactive text environment at the level of a standard elementary school science curriculum …
interactive text environment at the level of a standard elementary school science curriculum …
Hierarchy-aware multi-hop question answering over knowledge graphs
Knowledge graphs (KGs) have been widely used to enhance complex question answering
(QA). To understand complex questions, existing studies employ language models (LMs) to …
(QA). To understand complex questions, existing studies employ language models (LMs) to …
Joint reasoning with knowledge subgraphs for Multiple Choice Question Answering
Humans are able to reason from multiple sources to arrive at the correct answer. In the
context of Multiple Choice Question Answering (MCQA), knowledge graphs can provide …
context of Multiple Choice Question Answering (MCQA), knowledge graphs can provide …
Interpretable AMR-based question decomposition for multi-hop question answering
Effective multi-hop question answering (QA) requires reasoning over multiple scattered
paragraphs and providing explanations for answers. Most existing approaches cannot …
paragraphs and providing explanations for answers. Most existing approaches cannot …
A siamese neural network for learning semantically-informed sentence embeddings
Semantic representation is a way of expressing the meaning of a text that can be processed
by a machine to serve a particular natural language processing (NLP) task that usually …
by a machine to serve a particular natural language processing (NLP) task that usually …
Emotion classification in texts over graph neural networks: Semantic representation is better than syntactic
Social media is a widely used platform that provides a huge amount of user-generated
content that can be processed to extract information about users' emotions. This has …
content that can be processed to extract information about users' emotions. This has …
Assessing the Cross-linguistic Utility of Abstract Meaning Representation
S Wein, N Schneider - Computational Linguistics, 2024 - direct.mit.edu
Semantic representations capture the meaning of a text. Abstract Meaning Representation
(AMR), a type of semantic representation, focuses on predicate-argument structure and …
(AMR), a type of semantic representation, focuses on predicate-argument structure and …
Exploiting reasoning chains for multi-hop science question answering
We propose a novel Chain Guided Retriever-reader ({\tt CGR}) framework to model the
reasoning chain for multi-hop Science Question Answering. Our framework is capable of …
reasoning chain for multi-hop Science Question Answering. Our framework is capable of …