Papers Abstract Meaning Representation
“Abstract Meaning Representation” 태그가 달린 논문 219편 · 필터 해제
Abstract Meaning Representation for Hospital Discharge Summarization
The Achilles heel of Large Language Models (LLMs) is hallucination, which has drastic consequences for the clinical domain. This is particularly important with regards to automatically generating discharge summaries (a l…
Abstract Meaning RepresentationHallucinationWhen Does Meaning Backfire? Investigating the Role of AMRs in NLI
Natural Language Inference (NLI) relies heavily on adequately parsing the semantic content of the premise and hypothesis. In this work, we investigate whether adding semantic information in the form of an Abstract Meanin…
Abstract Meaning RepresentationNatural Language InferenceReassessing Graph Linearization for Sequence-to-sequence AMR Parsing: On the Advantages and Limitations of Triple-Based Encoding
Sequence-to-sequence models are widely used to train Abstract Meaning Representation (Banarescu et al., 2013, AMR) parsers. To train such models, AMR graphs have to be linearized into a one-line text format. While Penman…
Abstract Meaning RepresentationAMR ParsingSurvey of Abstract Meaning Representation: Then, Now, Future
This paper presents a survey of Abstract Meaning Representation (AMR), a semantic representation framework that captures the meaning of sentences through a graph-based structure. AMR represents sentences as rooted, direc…
Abstract Meaning RepresentationSurveytext-classificationText Classification+1SR-LLM: Rethinking the Structured Representation in Large Language Model
Structured representations, exemplified by Abstract Meaning Representation (AMR), have long been pivotal in computational linguistics. However, their role remains ambiguous in the Large Language Models (LLMs) era. Initia…
Abstract Meaning RepresentationLanguage ModelingLanguage ModellingLarge Language ModelGenerating Text from Uniform Meaning Representation
Uniform Meaning Representation (UMR) is a recently developed graph-based semantic representation, which expands on Abstract Meaning Representation (AMR) in a number of ways, in particular through the inclusion of documen…
Abstract Meaning RepresentationAMR-to-Text GenerationText GenerationNeurosymbolic Graph Enrichment for Grounded World Models
The development of artificial intelligence systems capable of understanding and reasoning about complex real-world scenarios is a significant challenge. In this work we present a novel approach to enhance and exploit LLM…
Abstract Meaning RepresentationImplicaturesNatural Language UnderstandingSCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus
We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. The corpus was constructed from multiple Wizard-of…
Abstract Meaning RepresentationHuman-Robot Dialogue Annotation for Multi-Modal Common Ground
In this paper, we describe the development of symbolic representations annotated on human-robot dialogue data to make dimensions of meaning accessible to autonomous systems participating in collaborative, natural languag…
Abstract Meaning RepresentationAMREx: AMR for Explainable Fact Verification
With the advent of social media networks and the vast amount of information circulating through them, automatic fact verification is an essential component to prevent the spread of misinformation. It is even more useful …
Abstract Meaning RepresentationClaim VerificationFact VerificationMisinformationA graph-based approach to extracting narrative signals from public discourse
Narratives are key interpretative devices by which humans make sense of political reality. As the significance of narratives for understanding current societal issues such as polarization and misinformation becomes incre…
Abstract Meaning RepresentationInformation RetrievalMisinformationFLAG: Financial Long Document Classification via AMR-based GNN
The advent of large language models (LLMs) has initiated much research into their various financial applications. However, in applying LLMs on long documents, semantic relations are not explicitly incorporated, and a ful…
Abstract Meaning RepresentationDocument ClassificationStock PredictionStock Trend Prediction+3Semantic Graphs for Syntactic Simplification: A Revisit from the Age of LLM
Symbolic sentence meaning representations, such as AMR (Abstract Meaning Representation) provide expressive and structured semantic graphs that act as intermediates that simplify downstream NLP tasks. However, the instru…
Abstract Meaning RepresentationInstruction FollowingSentenceMore Victories, Less Cooperation: Assessing Cicero's Diplomacy Play
The boardgame Diplomacy is a challenging setting for communicative and cooperative artificial intelligence. The most prominent communicative Diplomacy AI, Cicero, has excellent strategic abilities, exceeding human player…
Abstract Meaning RepresentationMASSIVE Multilingual Abstract Meaning Representation: A Dataset and Baselines for Hallucination Detection
Abstract Meaning Representation (AMR) is a semantic formalism that captures the core meaning of an utterance. There has been substantial work developing AMR corpora in English and more recently across languages, though t…
Abstract Meaning RepresentationHallucinationKnowledge Base Question AnsweringQuestion AnsweringDon't Forget to Connect! Improving RAG with Graph-based Reranking
Retrieval Augmented Generation (RAG) has greatly improved the performance of Large Language Model (LLM) responses by grounding generation with context from existing documents. These systems work well when documents are c…
Abstract Meaning RepresentationLanguage ModelingLanguage ModellingLarge Language Model+4Adapting Abstract Meaning Representation Parsing to the Clinical Narrative -- the SPRING THYME parser
This paper is dedicated to the design and evaluation of the first AMR parser tailored for clinical notes. Our objective was to facilitate the precise transformation of the clinical notes into structured AMR expressions, …
Abstract Meaning RepresentationAMR ParsingData AugmentationDomain AdaptationCompressing Long Context for Enhancing RAG with AMR-based Concept Distillation
Large Language Models (LLMs) have made significant strides in information acquisition. However, their overreliance on potentially flawed parametric knowledge leads to hallucinations and inaccuracies, particularly when ha…
Abstract Meaning RepresentationOpen-Domain Question AnsweringQuestion AnsweringRAG+2Analyzing the Role of Semantic Representations in the Era of Large Language Models
Traditionally, natural language processing (NLP) models often use a rich set of features created by linguistic expertise, such as semantic representations. However, in the era of large language models (LLMs), more and mo…
Abstract Meaning RepresentationIdentification of Entailment and Contradiction Relations between Natural Language Sentences: A Neurosymbolic Approach
Natural language inference (NLI), also known as Recognizing Textual Entailment (RTE), is an important aspect of natural language understanding. Most research now uses machine learning and deep learning to perform this ta…
Abstract Meaning RepresentationNatural Language InferenceNatural Language UnderstandingRTE