Papers with AMR
MASSIVE Multilingual Abstract Meaning Representation: A Dataset and Baselines for Hallucination Detection (2024.starsem-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic formalism that captures the core meaning of an utterance. |
| Approach: | They propose to use AMR to map meanings of 1,685 utterances to 50+ languages to build a dataset 20 times larger than existing resources. |
| Outcome: | The proposed dataset covers more languages, has more utterances, and has localized or translated entities for each language. |
Guiding AMR Parsing with Reverse Graph Linearization (2023.findings-emnlp)
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| Challenge: | Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a sentence. |
| Approach: | They propose a new framework that allows for reversed linearization of AMR graphs . they propose to combine sequence-to-sequence approaches with a linearized graph . |
| Outcome: | The proposed framework outperforms the best AMR parser by 0.8 and 0.5 Smatch scores on the AMR 2.0 and AMR 3.0 datasets. |
AMR-To-Text Generation with Graph Transformer (2020.tacl-1)
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| Challenge: | Abstract meaning representation (AMR)-to-text generation is challenging task for natural language processing. |
| Approach: | They propose a graph-to-sequence model that directly encodes AMR graphs and learns node representations. |
| Outcome: | The proposed model outperforms the current state-of-the-art neural approach by 1.5 BLEU points on LDC2015E86 and 4.8 BLUE points on the LDC2017T10 and achieves new state- of-the art performance. |
Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing (2024.findings-emnlp)
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| Challenge: | Cross-lingual AMR parsing is a task of predicting AMR graphs in a target language when training data is available only in . et al. (2018) evaluated meta-learning for cross-lingual parse in Croatian, Farsi, Korean, Chinese, and French. |
| Approach: | They propose to use meta-learning to tackle cross-lingual AMR parsing in a target language . they evaluate their models in k-shot scenarios and compare them to classical joint learning . |
| Outcome: | The proposed model performs better in 0-shot evaluation for Croatian, Farsi, Korean, Chinese, and French. |
Abstract Meaning Representation Guided Graph Encoding and Decoding for Joint Information Extraction (2021.naacl-main)
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| Challenge: | Abstract Meaning Representation (IE) and Information Extraction (IE), both focus on extracting the main information from natural language texts. |
| Approach: | They propose an AMR-guided framework for joint information extraction using a pre-trained AMR parser. |
| Outcome: | The proposed framework achieves state-of-the-art on all IE subtasks. |
Towards Turkish Abstract Meaning Representation (P19-2)
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| Challenge: | Abstract Meaning Representation (AMR) abstracts away from syntactic features such as word order and does not annotate every constituent in a sentence. |
| Approach: | They have built a first Turkish AMR corpus by hand-annotating 100 sentences from the novel "The Little Prince" they will use the results to prepare a Turkish AML annotation specification for future annotators. |
| Outcome: | The results of the study compare Turkish AMRs with English AMR annotations . the proposed framework is expected to be used in training future annotators. |
A Partially Rule-Based Approach to AMR Generation (N19-3)
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| Challenge: | Abstract Meaning Representation (AMR) is a representation of a sentence as a labeled graph . because of these abstractions, it can be difficult to generate from AMR back to a fluent English sentence . |
| Approach: | They propose a new approach to generating English text from Abstract Meaning Representation (AMR) it is largely rule-based, supplemented by a language model and simple statistical linearization models . they also address difficulties of automatically evaluating AMR generation systems . |
| Outcome: | The proposed approach produces a fluent English sentence with a high quality . it is difficult to generate from an AMR back to a sentence which preserves original meaning . |
AMR Parsing as Sequence-to-Graph Transduction (P19-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is the task of transducing natural language text into AMR, a graphbased formalism used for capturing sentence-level semantics. |
| Approach: | They propose a model that treats AMR parsing as sequence-to-graph transduction by aligner-free, and can be effectively trained with limited amounts of labeled AMR data. |
| Outcome: | The proposed model outperforms all previously reported SMATCH scores on AMR 2.0 (76.3%) and AMR 1.0 (70.2%). |
RepGraph: Visualising and Analysing Meaning Representation Graphs (2021.emnlp-demo)
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| Challenge: | Graph-based meaning representations provide rich semantic annotations, but visualising them clearly is more challenging than for fully lexicalized representations. |
| Approach: | They propose to use RepGraph to visualise, manipulate and analyse semantically parsed graph data in a JSON-based serialisation format. |
| Outcome: | The proposed visualisation and analysis tool supports DMRS, EDS, PTG, UCCA, and AMR semantic frameworks. |
Unsupervised Semantic Abstractive Summarization (P18-3)
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| Challenge: | Existing methods for abstractive summarization are limited in the sense that they can never generate human level summaries for large and complicated documents. |
| Approach: | They propose a pipeline for automatic abstractive summary generation using co-reference resolution and Meta Nodes. |
| Outcome: | The proposed pipeline outperforms the state-of-the-art method by 1.7% in node prediction. |
AMR Alignment for Morphologically-rich and Pro-drop Languages (2022.acl-srw)
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| Challenge: | Existing AMR aligners for English are not well suited for many languages where many concepts appear from morphologically-semantic elements. |
| Approach: | They propose to use a tree traversal approach to align AMR concepts from morphemes in a Turkish language. |
| Outcome: | The proposed aligner outperforms the existing aligners for English and Portuguese in terms of precision, recall and F1 score. |
A Dynamic, Interpreted CheckList for Meaning-oriented NLG Metric Evaluation – through the Lens of Semantic Similarity Rating (2022.starsem-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a popular NLG evaluation metric . however, it is difficult to judge where exactly such a metric fails . |
| Approach: | They propose a checklist for NLG evaluation metrics that focus on meaning by organizing them around meaning-relevant linguistic phenomena. |
| Outcome: | The proposed metric GraCo computes lexical cohesion graphs over AMR concepts. |
One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets (2021.starsem-1)
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| Challenge: | Existing semantic parsing datasets lack a single standard for meaning representations . lack of a standard led to the creation of plethora of datasets requiring expert annotators . |
| Approach: | They propose to use multi-task learning to unify different datasets and train a single model for them. |
| Outcome: | The proposed architectures yield better parsing accuracies and composition generalization than single-task models. |
SPRING Goes Online: End-to-End AMR Parsing and Generation (2021.emnlp-demo)
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| Challenge: | Abstract Meaning Representation (AMR) is a formalism for representing the semantics of natural language in a readable and hierarchical way. |
| Approach: | They present SPRING Online Services, a Web interface and RESTful APIs for their AMR parsing and generation system, SPRING (Symmetric PaRsIng aNd Generation). |
| Outcome: | The proposed system provides a highly interactive visualization platform and feedback mechanism to obtain user suggestions for further improvements of the system’s output. |
Incorporating EDS Graph for AMR Parsing (2021.starsem-1)
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| Challenge: | AMR is abstract and conceptual, while EDS is low level, closer to the lexical structures of the given sentences. |
| Approach: | They propose to add EDS graphs as additional semantic features to AMR parsers by adding transition-based parser to add LSTM layer and GCN layer. |
| Outcome: | The proposed parser adds EDS graphs as additional semantic features to boost performance . Currently the parsing accuracies for AMR are in low 80s, while they can be improved by adding more information from EDS. |
AMR-based Network for Aspect-based Sentiment Analysis (2023.acl-long)
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| Challenge: | Recent studies have used dependency trees to extract relation between aspects and contexts, but there is a potential mismatch between the dependency tree and sentiment classification as a semantic task. |
| Approach: | They propose to replace the syntactic dependency tree with a semantic structure to capture the relation between an aspect and a context. |
| Outcome: | The proposed model improves ABSA on four public datasets with 1.13% improvement over baselines. |
Dependency Patterns of Complex Sentences and Semantic Disambiguation for Abstract Meaning Representation Parsing (2021.starsem-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a sentence-level meaning representation based on predicate argument structure. |
| Approach: | They propose to use a dictionary to capture the structure of complex sentences . they train models on data derived from AMR and Wikipedia corpus . |
| Outcome: | The proposed model will be made public and the proposed patterns will be validated. |
Cross-Domain Sentiment Classification using Semantic Representation (2022.findings-emnlp)
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| Challenge: | Existing studies on cross-domain sentiment classification ignore the semantic relevance between domains. |
| Approach: | They propose to use Abstract Meaning Representation to help with cross-domain sentiment classification by combining sentence-level AMRs with text-graph interaction models. |
| Outcome: | The proposed model is effective over strong baselines and shows its importance over strong models. |
A Transition-Based Algorithm for Unrestricted AMR Parsing (N18-2)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation language to map the meaning of English sentences into directed, cycled, labeled graphs. |
| Approach: | They propose a left-to-right non-projective transition-based parser that handles cycles and reentrancy natively within the transition system itself. |
| Outcome: | The proposed algorithm handles reentrancy and arbitrary cycles natively, i.e. within the transition system itself. |
On the Role of Summary Content Units in Text Summarization Evaluation (2024.naacl-short)
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Marcel Nawrath, Agnieszka Nowak, Tristan Ratz, Danilo Walenta, Juri Opitz, Leonardo Ribeiro, João Sedoc, Daniel Deutsch, Simon Mille, Yixin Liu, Sebastian Gehrmann, Lining Zhang, Saad Mahamood, Miruna Clinciu, Khyathi Chandu, Yufang Hou
| Challenge: | a human written summary content unit (SCU) is used to judge the quality of a summary . a pyramid evaluation method is based on SCUs that decompose a reference summary into concise sentences . |
| Approach: | They propose to use automated SCUs to evaluate the quality of a candidate summary . they propose to generate SCU approximations from AMR meaning representations and large language models . |
| Outcome: | The proposed method can be fully automated, but lacks the human effort to validate it. |
Graph-to-Sequence Learning using Gated Graph Neural Networks (P18-1)
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| Challenge: | Existing approaches to graph-to-sequence learning ignore the full graph structure, discarding key information. |
| Approach: | They propose a graph-to-sequence learning model that encodes the full graph structure and an input transformation that allows nodes and edges to have their own hidden representations. |
| Outcome: | The proposed model outperforms baselines in generation from AMR graphs and syntax-based neural machine translation while retaining the full graph structure. |
A Corpus of German Abstract Meaning Representation (DeAMR) (2024.lrec-main)
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| Challenge: | Abstract Meaning Representations (AMRs) are semantic graphs that abstract away from surface syntax and capture the meaning of who does what to whom in a sentence. |
| Approach: | They propose to use German Abstract Meaning Representation (Deutsche AMR) to represent the structure and semantics of German. |
| Outcome: | The proposed framework is based on an annotated corpus of 400 DeAMR in German and is validated through inter-annotator agreement. |
Exploring Semantics in Pretrained Language Model Attention (2024.starsem-1)
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| Challenge: | Abstract Meaning Representations (AMRs) encode the semantics of sentences in the form of graphs. |
| Approach: | They propose to use attention heads of two LMs to detect semantic relations encoded in AMRs. |
| Outcome: | The proposed models detect semantic relations without fine tuning, using both unsupervised and supervised learning techniques. |
AMR Quality Rating with a Lightweight CNN (2020.aacl-main)
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| Challenge: | Structured semantic sentence representations are useful in various NLP tasks, but their quality can vary greatly and jeopardize their usefulness. |
| Approach: | They propose to transfer the AMR graph to the domain of images and create a convolutional neural network that imitates a human judge tasked with rating graph quality. |
| Outcome: | The proposed model can rate quality more accurately than strong baselines, in several quality dimensions, and reduces energy consumption. |
Bootstrapping Multilingual AMR with Contextual Word Alignments (2021.eacl-main)
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Janaki Sheth, Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Radu Florian, Salim Roukos, Todd Ward
| Challenge: | Abstract Meaning Representation (AMR) is a sentence-level graph that is biased towards English. |
| Approach: | They propose a technique for foreign-text-to-English AMR alignment using contextual word alignment between English and foreign language tokens. |
| Outcome: | The proposed technique outperforms the best results for German, Italian, Spanish and Chinese. |
SGL: Speaking the Graph Languages of Semantic Parsing via Multilingual Translation (2021.naacl-main)
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| Challenge: | Graph-based semantic parsing is one of the most promising general-purpose meaning representations . owing to this heterogeneity, most research focused on solutions specific to a given formalism . |
| Approach: | They propose a multilingual neural machine translation framework for Graph-based semantic parsing . they propose Graph2seq architecture that trains with an MNMT objective . |
| Outcome: | The proposed framework outperforms all competitors on cross-lingual parsing tasks. |
Inspecting Soundness of AMR Similarity Metrics in terms of Equivalence and Inequivalence (2024.starsem-1)
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| Challenge: | Existing Abstract Meaning Representation (AMR) similarity metrics have less investigated their soundness . |
| Approach: | They propose a new experimental method to evaluate soundness of AMR similarity metrics in terms of equivalence and inequivalentity. |
| Outcome: | The proposed method satisfies the soundness criteria of existing AMR similarity metrics and improves them by proposing a revised metric, SMATCH . |
AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction (2025.naacl-srw)
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| Challenge: | Existing in-context learning methods for relation extraction often overlook entity relationships . Existing methods for RE prioritize language similarity over structural similarity . |
| Approach: | They propose an AMR-enhanced retrieval-based ICL method for relation extraction . their method retrieves in-context examples based on semantic structure similarity . |
| Outcome: | The proposed method outperforms baselines on four English RE datasets and in the more demanding unsupervised setting. |
AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation (2024.naacl-long)
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| Challenge: | Existing methods for evaluating factual consistency of abstractive summarization lack coherence or error-type coverage. |
| Approach: | They propose a framework that generates perturbed summaries using Abstract Meaning Representations (AMRs) they use a selection module NegFilter to ensure the quality of the generated negative examples . |
| Outcome: | The proposed framework outperforms existing systems on the AggreFact-SOTA benchmark and provides high error-type coverage. |
A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering (2021.acl-short)
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Tahira Naseem, Srinivas Ravishankar, Nandana Mihindukulasooriya, Ibrahim Abdelaziz, Young-Suk Lee, Pavan Kapanipathi, Salim Roukos, Alfio Gliozzo, Alexander Gray
| Challenge: | Existing knowledge base question answering systems do not leverage the explicit semantic parse of the question text. |
| Approach: | They propose a transformer-based neural model that leverages the AMR semantic parse of a sentence. |
| Outcome: | The proposed model outperforms the state-of-the-art on 4 popular benchmark datasets. |
AMR Similarity Metrics from Principles (2020.tacl-1)
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| Challenge: | Abstract Meaning Representation (AMR) graphs are rooted, acyclic, directed, and edge-labeled. |
| Approach: | They propose a canonical Smatch metric that aligns variables of two graphs and assesses triple matches. |
| Outcome: | The proposed metric is more benevolent to only very slight meaning deviations and targets the fulfilment of all established criteria. |
Penman: An Open-Source Library and Tool for AMR Graphs (2020.acl-demos)
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| Challenge: | Abstract Meaning Representation encodes acyclic graphs in PENMAN notation format . the open-source Python library Penman provides a robust parser and functions for graph inspection and manipulation . |
| Approach: | They propose a framework for encoding acyclic graphs in PENMAN notation . the open-source Python library Penman provides a robust parser and functions for graph inspection and manipulation . |
| Outcome: | The open-source Python library Penman provides a robust parser, functions for graph inspection and manipulation, and functions for formatting graphs into PENMAN notation. |
Multitask Parsing Across Semantic Representations (P18-1)
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| Challenge: | UCCA parsing is a test case for multitask learning, with auxiliary tasks AMR, SDP and Universal Dependencies (UD) . Semantic parsers have arguably yet to reach their full potential due to the limited amount of semantically annotated training data. |
| Approach: | They propose a general transition-based parser that can parse UCCA, AMR, SDP and Universal Dependencies (UD) they use a transition-driven learning architecture and a uniform transition-basic learning architecture to train the parsers. |
| Outcome: | The proposed parser improves UCCA, AMR, SDP and Universal Dependencies (UD) parsing over training in English, German and French. |
CAMRA: Copilot for AMR Annotation (2023.emnlp-demo)
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| Challenge: | Abstract Meaning Representation (AMR) is a formalism for deep lexical semantic representation. |
| Approach: | They introduce a web-based tool for constructing AMR from natural language text . CAMRA incorporates AMR parser models as coding co-pilots . |
| Outcome: | The proposed tool is based on the prototyping of existing AMR editors and integrates Propbank roleset lookup as an autocomplete feature. |
Hierarchical Curriculum Learning for AMR Parsing (2022.acl-short)
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| Challenge: | Abstract Meaning Representation (AMR) parsing aims to translate sentences to semantic representation with a hierarchical structure, but there is a gap between their flat training objective and the hierarchic structure, which limits the model generalization. |
| Approach: | They propose a Hierarchical Curriculum Learning framework with Structure-level (SC) and Instance-level curricula (IC) that aims to translate sentences to semantic representation with a hierarchical structure. |
| Outcome: | Experiments on AMR2.0, AMR3.0, structure-complex and out-of-distribution situations confirm the effectiveness of the proposed framework. |
Transition-Based Chinese AMR Parsing (N18-2)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation where the meaning of a sentence is encoded as a rooted, directed and acyclic graph. |
| Approach: | They propose a transition-based AMR parsing framework for Chinese to be used in the next generation of AMR. |
| Outcome: | The proposed parser is based on the Chinese AMR bank. |
LiDARR: Linking Document AMRs with Referents Resolvers (2025.acl-demo)
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Jon Cai, Kristin Wright-Bettner, Zekun Zhao, Shafiuddin Rehan Ahmed, Abijith Trichur Ramachandran, Jeffrey Flanigan, Martha Palmer, James Martin
| Challenge: | Abstract Meaning Representation (AMR) is a formalism for semantic representation of natural language text. |
| Approach: | They propose a web tool for semantic annotation at the document level using Abstract Meaning Representation (AMR) it integrates an AMR-to-surface alignment model and a coreference resolution model into the tool . |
| Outcome: | The proposed tool simplifies the creation of knowledge graphs from natural language documents . it integrates an AMR-to-surface alignment model and coreference resolution model . |
Abstract Meaning Representation for Paraphrase Detection (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is ideal for paraphrase detection . it abstracts away from the syntactic realization of a sentence, and denotes only its meaning in a canonical form. |
| Approach: | They propose a technique that uses latent semantic analysis to translate sentences into AMR graphs . they show that the technique can be used to detect whether two sentences have the same meaning . |
| Outcome: | The proposed technique significantly advances state-of-the-art paraphrase detection for the Microsoft Research Paraphrase Corpus. |
Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation (2024.eacl-long)
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| Challenge: | Abstract Meaning Representation (AMR) can be used to reduce translationese in text . if translationeses are not addressed in training or test sets, evaluation scores can be overinflated . |
| Approach: | They propose to use Abstract Meaning Representation (AMR) to reduce translationese in translated texts. |
| Outcome: | The proposed approach outperforms other methods based on machine translation and paraphrase generation. |
SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features (2022.aacl-main)
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| Challenge: | Abstract Meaning Representation (S3BERT) embeddings are composed of explainable sub-embeddings that emphasize various sentence meaning features. |
| Approach: | They propose to induce Semantically Structured Sentence BERT embeddings (S3BERT) that emphasize various sentence meaning features. |
| Outcome: | The proposed model shows high correlation to human similarity ratings, but lacks interpretability. |
Accelerating Antibiotic Discovery with Large Language Models and Knowledge Graphs (2025.acl-industry)
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| Challenge: | Antimicrobial resistance (AMR) is one of the top ten global public health threats . pharmaceutical industries face high costs (over $1 billion), long timelines, and a high failure rate . |
| Approach: | They propose an LLM-based pipeline that acts as an alert system, detecting prior evidence of antibiotic activity to prevent costly rediscoveries. |
| Outcome: | The proposed system integrates literature on organisms and chemicals into a Knowledge Graph (KG), ensuring taxonomic resolution, synonym handling, and multi-level evidence classification. |
Semantic-based Pre-training for Dialogue Understanding (2022.coling-1)
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| Challenge: | Pre-trained language models are weak in understanding the main semantic meaning of a dialogue context. |
| Approach: | They propose a semantic-based framework that leverages explicit semantic knowledge to capture the core semantic information in dialogues during pre-training. |
| Outcome: | The proposed model is superior to existing models on chit-chats and task-oriented dialogues. |
Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters (2023.eacl-main)
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| Challenge: | Recent approaches to text generation from Abstract Meaning Representation (AMR) have been based on neural-centered encoderdecoder architectures. |
| Approach: | They propose a structure-aware adapter which injects the input graph connectivity within PLMs using Graph Neural Networks. |
| Outcome: | The proposed adapter is robust to a variety of approaches and can be used to generate Graph-to-Text representations. |
DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations (2022.acl-long)
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| Challenge: | Existing evaluation metrics for incoherent dialogues are insufficient to accurately reflect incohérence . despite the effectiveness of large pretrained language models, not everyone is into this type of work. |
| Approach: | They propose a Dialogue coherence Evaluation metric that uses Abstract Meaning Representation to apply semantic-level Manipulations for incoherent (negative) data generation. |
| Outcome: | The proposed evaluation metric achieves higher correlations with human judgments compared to baseline methods on dialog datasets by significant margins. |
Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation (2021.emnlp-main)
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| Challenge: | Recent work on multilingual AMR-to-text generation has focused on data augmentation strategies that utilize generated silver AMRs, but this assumes a high quality of generated AMR. |
| Approach: | They propose to combine gold AMR with silver AMRs to generate multilingual AMR annotations. |
| Outcome: | The proposed models outperform the current state of the art for German, Italian, Spanish, and Chinese by a large margin. |
Sequence-to-sequence AMR Parsing with Ancestor Information (2022.acl-short)
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| Challenge: | Abstract Meaning Representation (AMR) is a graph that encodes the semantic meaning of a sentence. |
| Approach: | They propose several strategies to add important ancestor information into a Transformer Decoder. |
| Outcome: | The proposed methods improve performance for both AMR 2.0 and AMR 3.0 datasets and achieve new state-of-the-art results. |
REMATCH: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity (2024.findings-naacl)
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| Challenge: | Existing AMR metrics are inefficient and struggle to capture semantic similarity . Existing metrics are not efficient and lack a systematic evaluation benchmark . |
| Approach: | They propose a new AMR similarity metric, rematch, which matches graphs structurally and semantically to each other. |
| Outcome: | The proposed metric is five times faster than the next most efficient metric. |
AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation (2024.emnlp-main)
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| Challenge: | proprietary large language models (LLMs) have demonstrated impressive code generation performance. |
| Approach: | They propose an adaptive module-based model that refines the direct response distillation process by modular decomposition and adaptive response evolution. |
| Outcome: | The proposed framework outperforms baseline model and code generation methods on three popular benchmarks. |
Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks (2020.acl-main)
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| Challenge: | Existing graph-to-sequence approaches use graph neural networks as encoders, but they lack the structure information needed to translate AMR into the graph-based data. |
| Approach: | They propose a graph-to-sequence task which aims to recover natural language from Abstract Meaning Representations (AMR) they adopt graph attention networks with higher-order neighborhood information to explore the edge relations in AMR graphs. |
| Outcome: | The proposed framework achieves state-of-the-art performance on English AMR benchmark datasets and is able to translate the AMR semantics into the natural language. |
XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation (2021.acl-long)
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| Challenge: | Abstract Meaning Representation (AMR) research is limited and challenging for languages other than English. |
| Approach: | They propose a cross-lingual pre-training approach for AMR parsing and text generation . they use an English-to-English parallel dataset and a multi-task learning approach . |
| Outcome: | The proposed approach outperforms baseline pre-training methods on English parsing and text generation tasks. |
Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation (2022.acl-short)
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| Challenge: | Automatic evaluation metrics score natural language generation systems based on how well they lexically align to humanannotated references. |
| Approach: | They propose to replace greedy alignments in BERTScore with optimized ones that replace the n-gram matching BERTAcore metrics with a token embedding to prevent domain mismatch. |
| Outcome: | The proposed metrics outperform cross-entropy and BLEU reward baselines on AMR-to-text generation. |
Inducing and Using Alignments for Transition-based AMR Parsing (2022.naacl-main)
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Andrew Drozdov, Jiawei Zhou, Radu Florian, Andrew McCallum, Tahira Naseem, Yoon Kim, Ramón Astudillo
| Challenge: | Abstract Meaning Representation parsers rely on node-to-word alignments, but lack the complexity of the pipeline. |
| Approach: | They propose a neural aligner for abstract meaning representation that learns node-to-word alignments without relying on pipelines. |
| Outcome: | The proposed approach improves accuracy and generalization from AMR2.0 to AMR3.0 corpora. |
One More Modality: Does Abstract Meaning Representation Benefit Visual Question Answering? (2025.findings-emnlp)
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| Challenge: | incorporating explicit semantic information, in the form of Abstract Meaning Representation graphs, can enhance VQA models. |
| Approach: | They augment two vision-language models with sentence- and document-level AMRs . they find that in well-resourced settings, models are negatively impacted by AMR . |
| Outcome: | The proposed model improves in well-resourced and low-resource settings with AMR graphs . the model achieves 13.1% relative gain using sentence-level AMRs compared with the smaller model . |
Weisfeiler-Leman in the Bamboo: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity (2021.tacl-1)
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| Challenge: | Existing metrics for assessing the similarity of abstract meaning representations (AMRs) have complementary strengths and weaknesses, and are expensive to implement. |
| Approach: | They propose a Benchmark for AMR Metrics based on overt objectives that can be used to assess graph-based similarity metrics. |
| Outcome: | The proposed metrics match contextualized substructures and induce n:m alignments between their nodes. |
Dialogue-AMR: Abstract Meaning Representation for Dialogue (2020.lrec-1)
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Claire Bonial, Lucia Donatelli, Mitchell Abrams, Stephanie M. Lukin, Stephen Tratz, Matthew Marge, Ron Artstein, David Traum, Clare Voss
| Challenge: | Abstract Meaning Representation (AMR) does not capture the illocutionary force or speaker’s intended contribution in the broader dialogue context. |
| Approach: | They propose a schema that enriches Abstract Meaning Representation (AMR) it provides a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. |
| Outcome: | The proposed schema provides a semantic representation for facilitating Natural Language Understanding (NLU) in human-robot dialogue systems. |
Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation (D18-1)
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| Challenge: | Recent work on abstractive summarization has made progress with neural encoder-decoder architectures, but these models lack explicit semantic modeling of the source document and its summary. |
| Approach: | They extend previous work on abstractive summarization using Abstract Meaning Representation (AMR) with a neural language generation stage which they guide using the source document. |
| Outcome: | The proposed approach improves summarization performance by 7.4 and 10.5 points in ROUGE-2 using gold standard AMR parses and parses obtained from an off-the-shelf parser respectively. |
Transition-based Parsing with Stack-Transformers (2020.findings-emnlp)
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| Challenge: | Existing parsing systems use local or global models of the parser state to improve performance. |
| Approach: | They propose to modify the sequence-to-sequence Transformer to model global or local parser states in transition-based parsing. |
| Outcome: | The proposed model significantly improves performance on dependency and Abstract Meaning Representation (AMR) parsing tasks. |
Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering (2021.findings-acl)
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| Challenge: | Existing approaches suffer from low confidence when retrieving evidence facts to fill the knowledge gap and lack transparent reasoning process. |
| Approach: | They propose a framework to exploit more valid facts while obtaining explainability for multi-hop question answering at web scale by dynamically constructing a semantic graph and reasoning over it. |
| Outcome: | The proposed framework surpasses existing approaches while maintaining high explainability on OpenBookQA and ARC-Challenge. |
Online Back-Parsing for AMR-to-Text Generation (2020.emnlp-main)
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| Challenge: | Abstract meaning representation (AMR) is a semantic graph representation that abstracts meaning away from a sentence. |
| Approach: | They propose a decoder that back predicts projected AMR graphs on target sentences . their results show superiority over previous state-of-the-art decoded graph Transformer . |
| Outcome: | The proposed model outperforms the state-of-the-art model on two AMR benchmarks. |
Abstract Meaning Representation for Multi-Document Summarization (C18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation of natural language based on linguistic theory . |
| Approach: | They propose to use Abstract Meaning Representation (AMR) as a content representation. |
| Outcome: | The proposed framework is fully data-driven and flexible. |
Cross-Lingual Abstract Meaning Representation Parsing (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) research has focused on English . Qualitative analysis shows that the new parsers overcome structural differences between the languages. |
| Approach: | They propose to use an AMR parser for English and parallel corpora to learn AMR for Italian, Spanish, German and Chinese. |
| Outcome: | The proposed method overcomes structural differences between the target languages and requires no gold standard data. |
A Structured Syntax-Semantics Interface for English-AMR Alignment (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) annotations do not require explicit mapping between elements of an AMR and the corresponding elements of the sentence that evoke them. |
| Approach: | They devised an expressive framework to align AMR graphs to dependency graphs . their framework explains how 97% of AMR edges are evoked by words or syntax . |
| Outcome: | The proposed framework explains how 97% of AMR edges are evoked by words or syntax. |
Cross-lingual AMR Aligner: Paying Attention to Cross-Attention (2023.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) graphs embed the semantics of a sentence in a directed acyclic graph, where concepts are represented by nodes, semantic relations between concepts by edges, and the co-references by reentrant nodes. |
| Approach: | They propose a novel aligner for Abstract Meaning Representation graphs that scales cross-lingually and can align units and spans in sentences of different languages. |
| Outcome: | The proposed aligner achieves state-of-the-art in the benchmarks and can scale cross-lingually. |
Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations (2022.findings-emnlp)
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| Challenge: | Existing approaches to syntactically controlled paraphrase generation require annotated paraphrase pairs for training and are costly to extend to new domains. |
| Approach: | They propose to leverage Abstract Meaning Representations (AMR) to improve the performance of unsupervised syntactically controlled paraphrase generation. |
| Outcome: | The proposed model generates more accurate syntactically controlled paraphrases, both quantitatively and qualitatively, compared to the existing unsupervised approaches. |
Can AMR Assist Legal and Logical Reasoning? (2022.findings-emnlp)
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| Challenge: | Abstract Meaning Representation (AMR) has been shown to be useful for many downstream tasks. |
| Approach: | They propose neural architectures that utilize linearised AMR graphs in combination with pre-trained language models to capture logical relationships on multiple choice question answering tasks. |
| Outcome: | The proposed models outperform text-only baselines but outperformed text models, suggesting complementary abilities. |
AMR Parsing via Graph-Sequence Iterative Inference (2020.acl-main)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is a broad-coverage semantic formalism that encodes the meaning of a sentence as a rooted, directed, labeled graph. |
| Approach: | They propose a model that treats AMR parsing as a series of dual decisions on the input sequence and the incrementally constructed graph. |
| Outcome: | The proposed model outperforms existing models by large margins on both input sequence and output graph. |
Semantically Inspired AMR Alignment for the Portuguese Language (2020.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) parsers require alignment between nodes and words of the sentence. |
| Approach: | They propose to use a more semantically matched word-concept pair to align graphs with words in Portuguese . they performed intrinsic and extrinsic evaluations and found it outperforms the English alignment strategies. |
| Outcome: | The proposed method outperforms the existing methods for English and achieves competitive results with a parser designed for the Portuguese language. |
Incorporating Graph Information in Transformer-based AMR Parsing (2023.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic graph abstraction for text representations. |
| Approach: | They propose a model and method that incorporates graph information into the learned representations of AMR by word-to-node alignment. |
| Outcome: | The proposed model improves AMR parsing performance by embedding graph information into the encoder at training time. |
Integrating Structural Semantic Knowledge for Enhanced Information Extraction Pre-training (2024.emnlp-main)
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| Challenge: | Existing pre-training methods focus on exploiting textual knowledge, which limits scalability and versatility of resulting models. |
| Approach: | They propose a pre-training framework that integrates structural semantic knowledge via contrastive learning. |
| Outcome: | The proposed framework outperforms state-of-the-art pre-training methods across multiple tasks. |
Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMR (2021.eacl-main)
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| Challenge: | Abstract meaning representations are typically evaluated using surface matching metrics . however, there are problems with these metrics, since they allow multiple surface realizations . |
| Approach: | They propose a decomposable metric that measures the distance between the original and reconstructed AMRs. |
| Outcome: | The proposed metric reduces the complexity of the evaluation process by allowing for multiple surface realizations. |
Exploiting Abstract Meaning Representation for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Existing work attempts to address these challenges using Pretrained Language Models (PLMs) but the diversity of surface form expressions can hinder the model’s ability to capture accurate correlations, especially when the context is lengthy and complex. |
| Approach: | They propose a method known as Graph-as-Token (GST) to incorporate AMRs into PLMs to assist the model in understanding complex semantic information. |
| Outcome: | The proposed method outperforms existing methods and significantly improves performance on both Natural Questions and TriviaQA. |
Avoiding Overlap in Data Augmentation for AMR-to-Text Generation (2021.acl-short)
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| Challenge: | Using unlabeled data to boost model performance is common practice in machine learning and natural language processing. |
| Approach: | They propose methods for excluding parts of Gigaword to remove overlap . they propose to use the AMR dataset for AMR-to-text generation . |
| Outcome: | The proposed approach leads to a more realistic evaluation of the task of AMR-to-text generation. |
Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing (2021.findings-acl)
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| Challenge: | Existing work on meaning representations for English and other languages finds that concepts in their predicted AMR graphs are less specific. |
| Approach: | They propose a cross-lingual AMR parser that can predict more precise concepts by translating translated texts and non-English texts. |
| Outcome: | The proposed model surpasses state-of-the-art parser by 10.6 points on Smatch F1 score. |
AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing (2023.acl-short)
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| Challenge: | et al., 2013) examines the current state-of-the-art in AMR parsing . current models violate structural constraints, but they can corrupt graphs . |
| Approach: | They propose two new ensemble strategies to improve AMR parsing robustness and reduce computational time. |
| Outcome: | The proposed methods improve robustness to structural constraints while reducing computational time. |
Multi-Relational Graph Transformer for Automatic Short Answer Grading (2022.naacl-main)
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| Challenge: | Existing methods for Automatic Short Answer Grading (ASAG) ignore structural context and therefore do not perform well. |
| Approach: | They propose a Multi-Relational Graph Transformer to prepare token representations considering the structural context of a sentence. |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on a dataset from an undergraduate computer science course. |
A Graph-to-Sequence Model for AMR-to-Text Generation (P18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic formalism that encodes the meaning of a sentence as a rooted, directed graph. |
| Approach: | They propose a neural graph-to-sequence model that leverages LSTM to encode a linearized AMR structure. |
| Outcome: | The proposed model outperforms existing methods on a benchmark. |
Towards AMR-BR: A SemBank for Brazilian Portuguese Language (L18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a recent and prominent meaning representation with good acceptance and several applications in the Natural Language Processing area. |
| Approach: | They propose to build an AMR annotated corpus for Brazilian Portuguese using an alignment-based approach. |
| Outcome: | The proposed corpus is based on the Little Prince book, which went into the public domain and explored some language-specific annotation issues. |
GPT-too: A Language-Model-First Approach for AMR-to-Text Generation (2020.acl-main)
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Manuel Mager, Ramón Fernandez Astudillo, Tahira Naseem, Md Arafat Sultan, Young-Suk Lee, Radu Florian, Salim Roukos
| Challenge: | Existing approaches to generating text from AMRs focus on training sequence-to-sequence or graph-tosequent models on annotated data. |
| Approach: | They propose a strong pre-trained language model with cycle consistency-based re-scoring to generate AMR text. |
| Outcome: | The proposed model outperforms existing methods on the English LDC2017T10 dataset. |
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation (2020.emnlp-main)
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| Challenge: | AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMRs) Graph Convolution Networks (GCNs) are not able to capture non-local information and follow a local (first-order) information aggregation scheme. |
| Approach: | They propose a dynamic fusion mechanism that captures richer non-local interactions . they propose weight tied convolutions and group graph convolution to reduce memory usage . |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets with significantly fewer parameters while maintaining the model capacity. |
Abstract Meaning Representation for Gesture (2022.lrec-1)
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| Challenge: | Abstract Meaning Representation (AMR) is an annotated graphbased representation that expresses the meaning of a sentence in terms of its predicate-argument structure. |
| Approach: | They propose an extension to Abstract Meaning Representation (AMR) that captures the meaning of gesture. |
| Outcome: | The proposed model is more challenging than standard AMR while integrating meaningful elements unique to gesture. |
AMR dependency parsing with a typed semantic algebra (P18-1)
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| Challenge: | Abstract Meaning Representations (AMRs) are graphs which describe the predicate-argument structure of a sentence. |
| Approach: | They propose a semantic parser which parses strings into tree representations of the compositional structure of an AMR graph. |
| Outcome: | The proposed parser outperforms baselines and standard neural techniques for supertagging and dependency tree parsing. |
SR-LLM: Rethinking the Structured Representation in Large Language Model (2025.acl-long)
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Jiahuan Zhang, Tianheng Wang, Ziyi Huang, Yulong Wu, Hanqing Wu, DongbaiChen DongbaiChen, Linfeng Song, Yue Zhang, Guozheng Rao, Kaicheng Yu
| Challenge: | Structured representations have long been pivotal in computational linguistics, but their role remains ambiguous in the Large Language Models (LLMs) era. |
| Approach: | They propose a framework that integrates structured representations into LLMs from training-free and training-dependent perspectives. |
| Outcome: | The proposed framework integrates structured representations through natural language descriptions in LLM prompts while augmenting the model’s inference capability through fine-tuning on linguistically described structured representation. |
Generalized Shortest-Paths Encoders for AMR-to-Text Generation (2020.coling-main)
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| Challenge: | a recent effort to encode graphs with gated convolutions provides local context, but distance messages travel is limited by number of encoder propagation steps. |
| Approach: | They propose to use generalized shortest-paths to train a graph encoder to learn shortest paths . they show that path diversity affects performance across levels of AMR connectivity . |
| Outcome: | The proposed model achieves a 1.4 BLEU and 1.8 chrF++ margin over a baseline model that encodes only pairwise-unique shortest paths. |
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs (2022.findings-naacl)
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| Challenge: | Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations. |
| Approach: | They propose to use auxiliary tasks which are semantically or formally related to enhance AMR parsing. |
| Outcome: | The proposed method achieves state-of-the-art performance on benchmarks especially in topology-related scores. |
XL-AMR: Enabling Cross-Lingual AMR Parsing with Transfer Learning Techniques (2020.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) is a popular formalism of natural language. |
| Approach: | They develop a cross-lingual AMR parser that can be trained on the produced data . they use transfer learning techniques to produce automatic AMR annotations across languages . |
| Outcome: | The proposed parser significantly surpasses those reported in Chinese, German, Italian and Spanish. |
Improving AMR Parsing with Sequence-to-Sequence Pre-training (2020.emnlp-main)
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| Challenge: | Abstract meaning representation (AMR) parsing is limited by the size of curated datasets. |
| Approach: | They propose a seq2seq pre-training approach to build pre-trained models on three relevant tasks. |
| Outcome: | The proposed model improves performance on three relevant tasks while maintaining the response of pre-trained models. |
Better Transition-Based AMR Parsing with a Refined Search Space (D18-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsers require a pipeline approach to learn concepts and relationships. |
| Approach: | They propose to use a transition-based search space to conduct a new compact AMR graph and an improved oracle to perform the search. |
| Outcome: | The proposed system achieves the state-of-the-art performance on various datasets with minimal additional information. |
The Role of Reentrancies in Abstract Meaning Representation Parsing (2020.findings-emnlp)
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| Challenge: | Abstract Meaning Representation (AMR) parsers make errors with respect to reentrancies, which complicates AMR parsing and requires specific transitions. |
| Approach: | They propose to categorize the types of errors AMR parsers make with respect to reentrancies and find that correcting these errors provides an in-crease of up to 5% Smatch in parsing perfor- mance and 20% in reen- trancy prediction. |
| Outcome: | The proposed formalism can predict reentrancies with 5% accuracy and 20% accuracy. |
Variational Graph Autoencoding as Cheap Supervision for AMR Coreference Resolution (2022.acl-long)
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| Challenge: | Abstract Meaning Representation (AMR) is a way to preserve the semantic meaning of a sentence in a graph. |
| Approach: | They propose a general pretraining method that leverages any general AMR corpus and even automatically parses AMR data to achieve performance gains of up to 6% absolute F1 points. |
| Outcome: | The proposed model significantly improves on the previous state-of-the-art model by up to 11% F1. |
Analyzing the Role of Semantic Representations in the Era of Large Language Models (2024.naacl-long)
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Zhijing Jin, Yuen Chen, Fernando Gonzalez Adauto, Jiarui Liu, Jiayi Zhang, Julian Michael, Bernhard Schölkopf, Mona Diab
| Challenge: | Existing studies show the benefits of semantic representations in NLP tasks . Existing work using AMR is concerned with trainable models . |
| Approach: | They propose an AMR-driven chain-of-thought prompting method that uses AMR . they propose to use it to predict which input examples AMR may help or hurt on . |
| Outcome: | The proposed method hurts performance more than it helps on five different tasks. |
Bootstrapping UMR Annotations for Arapaho from Language Documentation Resources (2024.lrec-main)
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| Challenge: | Uniform Meaning Representation (UMR) is a graph-based semantic labeling system . it is based on the AMR family and is designed to be uniformly applicable to typologically diverse languages. |
| Approach: | They propose methods for bootstrapping UMR annotations for a given language from existing resources and typical language documentation products. |
| Outcome: | The proposed method generates enough basic structure in UMR graphs to automate labeling to a significant extent. |
I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning (2020.coling-main)
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| Challenge: | a large amount of pre-defined commonsense knowledge is available for commonsensense reasoning . humans acquire commonsence in their lives, but machines cannot learn commonseense without assistance. |
| Approach: | They propose an AMR-ConceptNet-Pruned (ACP) graph that is pruned from a full integrated graph . they show that the ACP graph interprets the reasoning path and predicts the correct answer . |
| Outcome: | The proposed graph outperforms baseline models in the commonsenseQA task . it shows that the reasoning path can be interpreted with the relations and concepts provided by the graph . |
Factorising AMR generation through syntax (N19-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic annotation framework which abstracts away from the surface form of text to capture the core 'who did what to whom' structure. |
| Approach: | They propose to decompose the generation process into two steps: first generate a syntactic structure, and then generate the surface form. |
| Outcome: | The proposed approach generates meaning-preserving syntactic paraphrases of the same graph, as judged by humans. |
Multilingual AMR-to-Text Generation (2020.emnlp-main)
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| Challenge: | Existing work on generating text from structured data into English has focused on bridging the gap between structure and natural language (NL) and semantically underspecified input and fully specified output. |
| Approach: | They propose a multilingual approach that can decode into 21 different languages . they leverage advances in cross-lingual embeddings and pretraining to generate multilingual models . |
| Outcome: | The proposed model surpasses baselines that generate into one language in eighteen languages. |
The New Propbank: Aligning Propbank with AMR through POS Unification (L18-1)
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| Challenge: | Existing Propbank corpus converts sense labels to a format which is more compatible with AMR and more robust to sparsity. |
| Approach: | They propose a corpus which converts existing Propbank sense labels to a new unified format which is more compatible with AMR and more robust to sparsity. |
| Outcome: | The proposed format is more compatible with AMR and robust to sparsity. |
Neural Text Generation from Rich Semantic Representations (N19-1)
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| Challenge: | 2 is a neural model that maps a linearization of Dependency MRS to text . 1 is based on a BLEU score of 66.11 when trained on gold data . |
| Approach: | They propose to use Minimal Recursion Semantics to generate high-quality text from structured representations. |
| Outcome: | The proposed model achieves a BLEU score of 77.17 on the full test set and 83.37 on the subset of test data most closely matching the silver data domain. |
CALAMR: Component ALignment for Abstract Meaning Representation (2024.lrec-main)
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| Challenge: | Abstract meaning representation (AMR) graphs represent semantic structure in a syntactic independent way. |
| Approach: | They propose a method for graph alignment that can support summarization and evaluation. |
| Outcome: | The proposed method produces graphs that explain what is summarized through their alignments, which can be used to train graph based summarization learners. |
Multilingual AMR Parsing with Noisy Knowledge Distillation (2021.findings-emnlp)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is a broad-coverage semantic formalism that encodes the meaning of a sentence as a rooted, directed, and labeled graph. |
| Approach: | They propose to use existing English parser to learn and improve multilingual AMR parsers . their results show that noisy input and precise output are key to successful distillation . |
| Outcome: | The proposed model outperforms the current state-of-the-art English-only parser on four different languages. |
AMR-DA: Data Augmentation by Abstract Meaning Representation (2022.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation for NLP/NLU. |
| Approach: | They propose to use AMR-DA for data augmentation in NLP . they use sentence-level techniques like back translation and token-level methods like EDA . |
| Outcome: | The proposed method outperforms EDA and AEDA and improves on STS and text classification tasks. |
DocAMR: Multi-Sentence AMR Representation and Evaluation (2022.naacl-main)
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Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O’Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Astudillo, Radu Florian, Salim Roukos, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) graphs are compared to gold graphs by the Smatch metric, but lack a well-defined representation and evaluation. |
| Approach: | They propose an algorithm for deriving a unified graph representation using a super-sentential annotation method. |
| Outcome: | The proposed algorithm avoids the pitfalls of over-merging and lacks coherence from under merging. |
Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments (2021.acl-long)
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| Challenge: | Current algorithms for AMR parsing suffer from limited coverage and less-than-ideal accuracy . a new algorithm for AML uses unsupervised learning and heuristics to align components of AMR graphs to spans in English sentences . |
| Approach: | They propose algorithms for aligning components of Abstract Meaning Representation graphs to spans in English sentences. |
| Outcome: | The proposed approach covers a wider variety of AMR substructures than previously considered . it achieves higher coverage of nodes and edges, and does so with higher accuracy. |
AMR-TST: Abstract Meaning Representation-based Text Style Transfer (2023.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation that can enhance natural language generation (NLG) by providing a logical semantic input. |
| Approach: | They propose an AMR-based text style transfer technique that converts source text to an AML graph and generates transferred text based on the AMR graph modified by a TST policy named style rewriting. |
| Outcome: | The proposed method achieves state-of-the-art results compared with baseline models in automatic and human evaluations. |
An AMR Aligner Tuned by Transition-based Parser (D18-1)
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| Challenge: | Experimental results show that our AMR aligner outperforms the rule-based aligner by achieving higher alignment F1 score and consistently improving two open-source AMR parsers. |
| Approach: | They propose a rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser. |
| Outcome: | The proposed AMR aligner outperforms the current state-of-the-art parser by achieving higher alignment F1 score and consistently improving two open-source AMR parsers. |
Abstract Meaning Representation of Constructions: The More We Include, the Better the Representation (L18-1)
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Claire Bonial, Bianca Badarau, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Tim O’Gorman, Martha Palmer, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) uses a flexible pattern or template of multiple lexical items to provide semantic representation of certain constructions. |
| Approach: | They propose to expand the AMR project's lexicon of predicate senses to include entries for a growing set of constructions. |
| Outcome: | The proposed approach provides coverage for the annotation of certain types of constructions. |
Pushing the Limits of AMR Parsing with Self-Learning (2020.findings-emnlp)
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Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Revanth Gangi Reddy, Radu Florian, Salim Roukos
| Challenge: | Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years due to the impact of transfer learning and the development of novel architectures specific to AMR. |
| Approach: | They propose to use AMR annotations to generate synthetic text and refine actions oracle without additional human annotations for AMR parsing. |
| Outcome: | The proposed models improve on AMR 1.0 and 2.0 without human annotations. |
Enhancing AMR-to-Text Generation with Dual Graph Representations (D19-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a linguistically-grounded semantic formalism that represents the meaning of a sentence as a directed graph. |
| Approach: | They propose a graph-to-sequence model that encodes different but complementary perspectives of the structural information contained in the graph. |
| Outcome: | The proposed model achieves state-of-the-art results on two AMR datasets. |
Text Generation Model Enhanced with Semantic Information in Aspect Category Sentiment Analysis (2023.findings-acl)
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| Challenge: | Existing methods for ACSA fail to model relations of target words and opinion words in a sentence including multiple aspects. |
| Approach: | They propose to incorporate AMR into a text generation model to model relations of target words and opinion words in a sentence including multiple aspects. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three datasets. |
End-to-End AMR Coreference Resolution (2021.acl-long)
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| Challenge: | Existing work on AMR focuses on individual sentences, but there is a need for multi-sentence AMRs. |
| Approach: | They propose to use an end-to-end AMR coreference resolution model to generate multi-sentence AMRs. |
| Outcome: | The proposed model reduces error propagation and is more robust for both in- and out-domain situations. |
Accounting for Language Effect in the Evaluation of Cross-lingual AMR Parsers (2022.coling-1)
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| Challenge: | Existing multilingual AMR evaluation metrics are not available for cross-lingual parsers . existing studies show that source language has a dramatic effect on cross-linguistic AMRs . |
| Approach: | They propose to use three multilingual adaptations of monolingual AMR evaluation metrics to evaluate cross-lingual AML parsers. |
| Outcome: | The proposed metric is the most highly correlated to english AMRs, while the most correlated is S2match. |
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering (2021.findings-acl)
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Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramón Fernandez Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-Suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Gangi Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu
| Challenge: | Existing approaches face challenges including complex question understanding and lack of large end-to-end training datasets. |
| Approach: | They propose a modular knowledge base question answering system that leverages AMR parses for task-independent question understanding. |
| Outcome: | The proposed system achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia. |
Semantic Representation for Dialogue Modeling (2021.acl-long)
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| Challenge: | Existing models for dialogue modeling lack ability to represent core semantics, such as ignoring important entities. |
| Approach: | They develop an algorithm to construct dialogue-level AMR graphs from sentence-level data and explore two ways to incorporate AMRs into dialogue modeling. |
| Outcome: | The proposed model is superior to existing models on dialogue understanding and response generation tasks. |
Revisiting Pivot-Based Paraphrase Generation: Language Is Not the Only Optional Pivot (2021.emnlp-main)
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| Challenge: | Existing methods for paraphrase generation rely on language as the pivot . however, there is no evidence that parallel data of paraphrases is needed for paraphrasing. |
| Approach: | They propose to use semantic and syntactic representations as pivot for paraphrase generation. |
| Outcome: | The proposed method can generate paraphrases with better quality than using language as pivot. |
Structural Adapters in Pretrained Language Models for AMR-to-Text Generation (2021.emnlp-main)
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| Challenge: | Pretrained language models (PLMs) have advanced graph-to-text generation, but efficient encoding of graph structure is challenging because of the nature of the data. |
| Approach: | They propose a method to encode graph structure into pretrained language models by training only graph structure-aware adapter parameters. |
| Outcome: | The proposed method outperforms the state-of-the-art on two AMR-to-text datasets, training only 5.1% of the adapter parameters. |
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning (2024.findings-acl)
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Qiming Bao, Alex Peng, Zhenyun Deng, Wanjun Zhong, Gael Gendron, Timothy Pistotti, Neset Tan, Nathan Young, Yang Chen, Yonghua Zhu, Paul Denny, Michael Witbrock, Jiamou Liu
| Challenge: | Empirical evidence shows that our proposed method improves performance across seven downstream tasks. |
| Approach: | They propose a logic-driven data augmentation approach that converts text into AMR graphs and converts them back into text to create augmented data. |
| Outcome: | The proposed method leads on the ReClor leaderboard and improves on seven downstream tasks. |
Construct a Sense-Frame Aligned Predicate Lexicon for Chinese AMR Corpus (2020.lrec-1)
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| Challenge: | Existing lexicons blur senses and frames of predicates, which needs to be refined to meet word sense disambiguation and event extraction tasks. |
| Approach: | They propose to construct a predicate lexicon for Chinese AMR corpus with 14,389 senses and 10,800 frames of 8,470 words. |
| Outcome: | The proposed lexicon includes 14,389 senses and 10,800 frames of 8,470 words. |
Structural Neural Encoders for AMR-to-text Generation (N19-1)
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| Challenge: | Abstract Meaning Representation (AMR) graphs are graphs, rather than trees, because they contain reentrant nodes with multiple parents. |
| Approach: | They propose to use sequence-to-sequence models that encode AMR graphs into vector representations to generate sentences from AMRs. |
| Outcome: | The proposed model outperforms tree encoders in the AMR-to-text generation task by 24.40 points. |
A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction (2022.naacl-main)
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| Challenge: | Existing studies aim at extracting event arguments from a single sentence . document-level event extraction still remains under-explored . |
| Approach: | They propose a two-stream abstract meaning representation enhanced extraction model to extract event arguments from an entire document. |
| Outcome: | The proposed model outperforms state-of-the-art in extracting event arguments from documents by 2.54 F1 and 5.13 F1 on public RAMS and WikiEvents datasets. |
A Survey of AMR Applications (2024.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation that takes the form of a rooted, directed graph. |
| Approach: | They analyze more than 100 papers which use Abstract Meaning Representation (AMR) they highlight the range of applications for which AMR has been harnessed and techniques for incorporating it . they also highlight broader AMR engineering patterns and outline areas of future work that seem ripe for AMR incorporation. |
| Outcome: | The results highlight the range of applications for which AMR has been harnessed and the techniques for incorporating it into those applications. |
Broad-Coverage Semantic Parsing as Transduction (D19-1)
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| Challenge: | Existing approaches to broad-coverage semantic parsing are not applicable to all frameworks because of the lack of explicit alignments between tokens in the sentence and nodes in the semantic graph. |
| Approach: | They propose a transduction parsing paradigm that unifies different broad-coverage semantic parsers into a paradigm that leverages multiple attention mechanisms to build meaning representation. |
| Outcome: | The proposed approach improves state-of-the-art on AMR, SDP and UCCA and is competitive with the state- of-the art on SDP. |
Core Semantic First: A Top-down Approach for AMR Parsing (D19-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is a semantic formalism that encodes the meaning of a sentence as a rooted labeled directed graph. |
| Approach: | They propose a scheme for parsing text into its Abstract Meaning Representation (AMR) using Graph Spanning based Parsing. |
| Outcome: | The proposed scheme achieves state-of-the-art on the latest AMR sembank and no heuristic graph re-categorization is adopted. |
Maximum Bayes Smatch Ensemble Distillation for AMR Parsing (2022.naacl-main)
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| Challenge: | AMR parsing has experienced an unprecendented increase in performance in the last three years due to a mixture of effects including architecture improvements and transfer learning. |
| Approach: | They propose to combine Smatch-based ensembling techniques with ensemble distillation to overcome this diminishing returns of silver data. |
| Outcome: | The proposed technique can produce gains rivaling those of human annotated data for QALD-9 and achieve a new state-of-the-art for BioAMR. |
AMR Parsing with Latent Structural Information (2020.acl-main)
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| Challenge: | Abstract Meaning Representations (AMRs) capture sentence-level semantics structural representations to broad-coverage natural sentences. |
| Approach: | They investigate parsing AMR with explicit dependency structures and interpretable latent structures. |
| Outcome: | The proposed model achieves best results on both AMR 2.0 and AMR 1.0 . the proposed model has been adopted in downstream NLP tasks, including text summarization and question answering. |
Stacked AMR Parsing with Silver Data (2021.findings-emnlp)
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| Challenge: | Lack of large-scale annotated data is one main challenge for abstract meaning representation (AMR) parsing. |
| Approach: | They propose to use silver data to train a pre-trained abstract meaning representation model. |
| Outcome: | The proposed model outperforms previous models on the AMR2.0 dataset and is faster than the SOTA model. |
Graph Pre-training for AMR Parsing and Generation (2022.acl-long)
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| Challenge: | Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. |
| Approach: | They propose two graph auto-encoding strategies for graph-to-graph pre-training and four tasks to integrate text and graph information during pre-tuning to improve structure awareness. |
| Outcome: | The proposed model is superior to pre-trained language models on AMR parsing and AMR-to-text generation tasks. |
A Human Evaluation of AMR-to-English Generation Systems (2020.coling-main)
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| Challenge: | a recent human evaluation of AMR generation systems is compared to automated metrics. |
| Approach: | They propose a human evaluation which collects fluency and adequacy scores and categorization of error types for AMR generation systems. |
| Outcome: | The results show that human evaluations are more nuanced than automated metrics. |
Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation (2022.emnlp-main)
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| Challenge: | Existing studies on multilingual sentence embeddings focus on cross-lingual semantic textual similarity and transfer tasks. |
| Approach: | They propose a method to improve existing multilingual sentence embeddings with Abstract Meaning Representation (AMR) . they compare existing multi-lingual sentence embedded with AMR and improve their versions by reducing the surface variations across different languages and expressions. |
| Outcome: | The proposed method improves state-of-the-art multilingual sentence embeddings on transfer tasks and semantic textual similarity tests. |
AMR Parsing with Action-Pointer Transformer (2021.naacl-main)
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| Challenge: | Abstract Meaning Representation parsing is a sentence-to-graph prediction task . graph nodes are semantically based on one or more sentence tokens, so implicit alignments can be derived. |
| Approach: | They propose a transition-based system that decouples hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments. |
| Outcome: | The proposed system achieves the second best Smatch score on AMR 2.0 (81.8) it decouples source tokens from node representations and addresses alignments, but lacks expressiveness. |
SemBleu: A Robust Metric for AMR Parsing Evaluation (P19-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic formalism where the meaning of a sentence is encoded as a rooted, directed graph. |
| Approach: | They propose a metric that extends SMATCH to parse AMRs and does not suffer from search errors. |
| Outcome: | The proposed metric does not suffer from search errors and considers non-local correspondences in addition to local ones. |
Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction (2023.findings-acl)
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| Challenge: | Popular GEC models use large-scale synthetic corpora or use a large number of human-designed rules. |
| Approach: | They propose a model that incorporates denoised AMR as additional knowledge to get AMRs more reliable. |
| Outcome: | The proposed model reduces training time by 32% while inference time is comparable. |
Compositional Semantic Parsing across Graphbanks (P19-1)
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| Challenge: | Existing semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. |
| Approach: | They propose a compositional neural semantic parser which achieves competitive accuracies across graphbanks. |
| Outcome: | The proposed system achieves competitive accuracies across a variety of graphbanks. |
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning (P19-1)
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| Challenge: | Abstract meaning representations (AMRs) are labeled directed acyclic graphs that represent a non intersentential abstraction of natural language with broad-coverage semantic representations. |
| Approach: | They build upon a transition-based AMR parser that uses Stack-LSTMs and augment training with policy learning. |
| Outcome: | The proposed parser performs comparable to the best published parsers. |
BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning (2022.coling-1)
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| Challenge: | Existing approaches to AMR focus on one-side improvements despite the duality of the two tasks . instead, we propose data-efficient Bidirectional Bayesian learning (BiBL) to facilitate bidirectional information transition. |
| Approach: | They propose a data-efficient bidirectional Bayesian learning approach to facilitate bidirectional information transition by adopting a single-stage multitasking strategy. |
| Outcome: | The proposed model outperforms existing models on benchmark datasets without extra training data. |
Annotating Abstract Meaning Representations for Spanish (L18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation language for natural language processing. |
| Approach: | They propose a method that would lay the groundwork for building a large semantic bank for Spanish . they propose to use a database to annotate AMRs for other languages . |
| Outcome: | The proposed method would lay the groundwork for building a large semantic bank for Spanish and guide those who would like to implement it for other languages. |
Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation (2021.acl-long)
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| Challenge: | Compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. |
| Approach: | They propose a novel biomedical Information Extraction model to extract scientific entities and events from English research papers using Abstract Meaning Representation (AMR) they construct a sentence-level knowledge graph from an external knowledge base and encode it to improve the model's understanding of complex scientific concepts. |
| Outcome: | The proposed model can extract scientific entities and events from scientific literature and improve its understanding of complex scientific concepts. |
CLEVE: Contrastive Pre-training for Event Extraction (2021.acl-long)
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| Challenge: | Existing EE methods do not model event characteristics from large unsupervised data. |
| Approach: | They propose a contrastive pre-training framework for event extraction to better learn event knowledge from large unsupervised data and their semantic structures. |
| Outcome: | The proposed framework improves on ACE 2005 and MAVEN datasets on event extraction tasks. |
World Knowledge for Abstract Meaning Representation Parsing (L18-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsers are based on annotated graphs, but there is still room for improvement . |
| Approach: | They examine the role played by world knowledge in parsing errors in a state-of-the-art parser . they examine the effects of different types of world knowledge on parsers . |
| Outcome: | The proposed model improves on multiple fine-grained metrics, including a 6% increase in named entity F-score, and provides insight into the potential of world knowledge for future work in Abstract Meaning Representation parsing. |
Efficient AMR Parsing with CLAP: Compact Linearization with an Adaptable Parser (2024.lrec-main)
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| Challenge: | Abstract Meaning Representation (AMR) parsers face efficiency challenges because of their large model size and computational time, which limit their accessibility within the research community. |
| Approach: | They propose a novel linearization system that simplifies encoding and reduces the number of tokens by between 40% and 50%. |
| Outcome: | The proposed system reduces the number of tokens by 40% and 50% while maintaining high performance while reducing training and inference times. |
Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing (2021.emnlp-main)
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| Challenge: | Recent work shows that pre-trained sequence-to-sequence Transformer models are effective in predicting linearized Abstract Meaning Representation graphs. |
| Approach: | They propose a structure-aware transition-based approach to AMR parsing that integrates general pre-trained sequence-to-sequence language models with a structured transition set. |
| Outcome: | The proposed approach retains the desirable properties of previous approaches while reaching the new parsing state of the art for AMR 2.0. |
Using Structured Content Plans for Fine-grained Syntactic Control in Pretrained Language Model Generation (2022.coling-1)
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| Challenge: | Large pretrained language models can generate powerful text but cannot be controlled at a sub-sentential level. |
| Approach: | They propose to make such fine-grained control possible in pretrained LMs by generating text directly from a semantic representation, Abstract Meaning Representation (BART), which is augmented at the node level with syntactic control tags. |
| Outcome: | The proposed method can generate text from a semantic representation, which is augmented at the node level with syntactic control tags. |
Encoding Gesture in Multimodal Dialogue: Creating a Corpus of Multimodal AMR (2024.lrec-main)
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| Challenge: | Abstract Meaning Representation (AMR) was designed to represent sentence meaning in English text, but recent research has explored its adaptation to broader domains, including documents, dialogues, spatial information, cross-lingual tasks, and gesture. |
| Approach: | They propose to annotate a multimodal (speech and gesture) AMR corpus in a task-based setting and capture coreference relationships across modalities. |
| Outcome: | The proposed corpus captures coreference relationships across modalities, enabling fine-grained analysis of how gesture and natural language interact. |
QASem Parsing: Text-to-text Modeling of QA-based Semantics (2022.emnlp-main)
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| Challenge: | Existing work suggests the appeals of incorporating explicit semantic representations into NLP . semi-structured natural language structures provide an intermediate meaning-capturing representation . |
| Approach: | They propose a semi-structured natural-language representation of textual information . they examine input and output linearization strategies and multitask learning . |
| Outcome: | The proposed model is based on pre-trained sequence-to-sequence language models . it is easy to use and can be used for downstream tasks that benefit from it . |
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation (D19-1)
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| Challenge: | Recent studies on AMR-to-text generation formalize the task as a sequence-tosequence learning problem . previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. |
| Approach: | They propose a structure-aware self-attention approach to model the relations between indirectly connected concepts in the seq2seq model. |
| Outcome: | The proposed approach outperforms the state-of-the-art on English AMR benchmarks . it significantly outperformed the state of the art on the benchmarks, with 29.66 and 31.82 BLEU scores . |
“You Are An Expert Linguistic Annotator”: Limits of LLMs as Analyzers of Abstract Meaning Representation (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) demonstrate proficiency and fluency in the use of language, but do they have the linguistic knowledge to serve as an expert linguistic annotator? |
| Approach: | They examine the successes and limitations of large language models using the Abstract Meaning Representation (AMR) parsing formalism. |
| Outcome: | The proposed models can reproduce the basic format of AMR, as well as some core event, argument, and modifier structure, but they have virtually no fully accurate parses. |
AMR Parsing with Causal Hierarchical Attention and Pointers (2023.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation of natural language sentences. |
| Approach: | They propose a new target form of AMR parsing and a model which integrates structural localities into the Transformer decoder. |
| Outcome: | The proposed model outperforms baseline models on four out of five benchmarks in the setting of no additional data. |
Concept rather than Document: Context Compression via AMR-based Conceptual Entropy (2026.findings-acl)
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| Challenge: | Existing methods rely on lexical or token-level features that fragment semantic units and fail to capture conceptually essential content. |
| Approach: | They propose an unsupervised framework leveraging Abstract Meaning Representation to preserve essential information while filtering irrelevant text. |
| Outcome: | The proposed framework outperforms RAG and existing baselines while preserving essential information while filtering irrelevant text. |
Spatial AMR: Expanded Spatial Annotation in the Context of a Grounded Minecraft Corpus (2020.lrec-1)
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| Challenge: | Existing annotation tools for spatial relations capture fine-grained semantics and pragmatics derived from spatial information. |
| Approach: | They propose an extension to the Abstract Meaning Representation annotation schema that captures fine-grained spatial information in grounded corpora. |
| Outcome: | The proposed tool can handle fine-grained spatial relationships grounded in quantized space. |
T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation (2022.emnlp-main)
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| Challenge: | Unavailability of parallel corpora for training text style transfer models is a challenge but common . a large corpus of parallel data is not available for text style transfers . |
| Approach: | They propose to use AMR as an intermediate style agnostic representation to train TST models. |
| Outcome: | The proposed model outperforms state-of-the-art models in the style agnostic task. |
AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model (2023.acl-long)
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| Challenge: | Existing generation-based EAE models focus on problem re-formulation and prompt design without incorporating additional information that has been shown to be effective for classification-based models. |
| Approach: | They propose to incorporate AMR into generation-based EAE models by generating AMR-aware prefixes for every layer of the generation model. |
| Outcome: | The proposed model generates AMR-aware prefixes for every layer of the generation model and improves the generation. |
Align-smatch: A Novel Evaluation Method for Chinese Abstract Meaning Representation Parsing based on Alignment of Concept and Relation (2022.lrec-1)
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| Challenge: | Abstract Meaning Representation abstracts the meaning of sentences into a single-rooted, acyclic and directed graph. |
| Approach: | They propose to use a metric to evaluate concept alignment and relation alignment to improve Chinese AMR parsing evaluation methods. |
| Outcome: | The proposed method is more robust and compatible with concept alignment and relation alignment and more robust in evaluating arcs. |
Heterogeneous Graph Transformer for Graph-to-Sequence Learning (2020.acl-main)
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| Challenge: | Recent studies ignore the indirect relations between distance nodes, or treat indirect relations and direct relations in the same way. |
| Approach: | They propose a graph-to-sequence (Graph2Seq) encoder which models graph structure to model different relations in individual subgraphs of the original graph. |
| Outcome: | The proposed model outperforms the state-of-the-art on all four benchmarks of AMR-to-text generation and syntax-based neural machine translation. |
Learning from a Friend: Improving Event Extraction via Self-Training with Feedback from Abstract Meaning Representation (2023.findings-acl)
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| Challenge: | Existing data scarcity hinders the progress of event extraction, authors say . ACE-052 has 10 of the 33 event types with less than 80 annotations, authors claim . |
| Approach: | They propose a self-training with feedback framework that leverages large-scale unlabeled data to acquire feedback for each new event prediction from the unlabed data. |
| Outcome: | The proposed framework improves event extraction models even when unlabeled data are unavailable. |
AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite (2023.emnlp-main)
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| Challenge: | Abstract Meaning Representation parsers have improved in recent years, but not solved. |
| Approach: | They propose an evaluation suite that evaluates AMR parsers on a range of phenomena . they find that current parser outputs are far from satisfactory . |
| Outcome: | The proposed evaluation suite reveals the abilities and shortcomings of current parsers. |
GLAMR: Augmenting AMR with GL-VerbNet Event Structure (2024.lrec-main)
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Jingxuan Tu, Timothy Obiso, Bingyang Ye, Kyeongmin Rim, Keer Xu, Liulu Yue, Susan Windisch Brown, Martha Palmer, James Pustejovsky
| Challenge: | Abstract Meaning Representation (AMR) is a general-purpose semantic encoding for language. |
| Approach: | They propose an AMR interpretation of Generative Lexicon semantic components using a verb-net-encoded verb-node graph. |
| Outcome: | The proposed extension is compatible with current AMR specification and can be automated. |
Creation of a Balanced State-of-the-Art Multilayer Corpus for NLU (L18-1)
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Normunds Gruzitis, Lauma Pretkalnina, Baiba Saulite, Laura Rituma, Gunta Nespore-Berzkalne, Arturs Znotins, Peteris Paikens
| Challenge: | Using full stack of language resources, we are creating a balanced text corpus for Latvian. |
| Approach: | They propose to create a syntactically and semantically annotated multilayered corpus for Latvian . they use widely acknowledged and cross-lingual representations for the corpus . |
| Outcome: | The proposed corpus adopts widely recognized and cross-lingual representations for natural language understanding and generation in Latvian. |
A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing (2021.emnlp-main)
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| Challenge: | Abstract Meaning Representations (AMR) represents sentence meaning as a directed acyclic graph. |
| Approach: | They propose to treat alignment and segmentation as latent variables and induce them as part of end-to-end training. |
| Outcome: | The proposed model achieves significant performance gains over a 'greedy' segmentation heuristic. |
From Spatial Relations to Spatial Configurations (2020.lrec-1)
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| Challenge: | Existing spatial representations are not sufficient for describing complex spatial configurations. |
| Approach: | They propose to integrate existing spatial representation languages with an annotation schema to extend the capabilities of existing ones. |
| Outcome: | The proposed language can represent a large set of spatial concepts crucial for reasoning . it integrates with the Abstract Meaning Representation (AMR) annotation schema and annotates text from diverse datasets . |
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction (2023.acl-long)
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| Challenge: | Recent work has introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE) however, in these works AMR is used only implicitly, for instance, as additional features or training signals. |
| Approach: | They propose a novel AMR-based graph structure which uses graph neural networks to find event arguments from unstructured text. |
| Outcome: | The proposed graph structure outperforms the state-of-the-art models by 3.63pt and 2.33pt F1 and reduces inference time by 56%. |
CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks (2023.emnlp-main)
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| Challenge: | Chain-of-Thought prompting is popular in reasoning tasks, but its application to Large Language Models (LLMs) in Natural Language Understanding (NLU) is under-explored. |
| Approach: | They propose a Coarse-to-Fine Chain-of-Thought approach that breaks down NLU tasks into multiple reasoning steps where LLMs can learn to acquire essential concepts. |
| Outcome: | The proposed approach is effective in assisting the LLMs adapt to multi-grained NLU tasks under zero-shot and few-shot multi-domain settings. |
Cross-domain Generalization for AMR Parsing (2022.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input. |
| Approach: | They evaluate five representative AMR parsers on five domains and analyze challenges to cross-domain parsing. |
| Outcome: | The proposed method reduces the domain distribution divergence of text and AMR features on two out-of-domain sets. |
Semantic Simplification for Sentiment Classification (2022.emnlp-main)
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| Challenge: | Recent work on document-level sentiment classification has shown that the sentiment in the original text is often hard to capture . previous studies focus on predicting the overall sentiment from original text using statistical or neural models, but these methods either heavily rely on human knowledge or suffer from the complex structure of the text. |
| Approach: | They propose a document-level sentiment classification model that enhances the original text with a simplified clause to intensify its sentiment. |
| Outcome: | Empirical studies show that the proposed model over strong baselines is effective over several strong baseline models. |
Improving Long Dialogue Summarization with Semantic Graph Representation (2023.findings-acl)
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| Challenge: | Existing algorithms for abstractive summarization of short dialogues are challenging . however, they can generate high-quality summaries for long dialogues . |
| Approach: | They propose an algorithm that processes complete dialogues into topic-segment-level Abstract Meaning Representation graphs . they propose a pretrained LLM that exploits the text to leverage graph semantics a new text-graph attention . |
| Outcome: | The proposed algorithm outperforms state-of-the-art models on multiple long dialogue summarization datasets . it also generates additional training signals that facilitate graph feature encoding and content selection. |
PAM: Paraphrase AMR-Centric Evaluation Metric (2025.findings-acl)
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| Challenge: | Current evaluation metrics for paraphrase generation are based on borrowed metrics from text-to-text tasks . this is not ideal for paraphrasing as we typically want variation in the lexicon while persisting semantics. |
| Approach: | They propose a Paraphrase AMR-Centric Evaluation Metric that uses AMR graphs extracted from the input text to evaluate paraphrases. |
| Outcome: | The proposed evaluation metric improves on different semantic textual similarity datasets on paraphrases with human semantic scores. |
Evaluate AMR Graph Similarity via Self-supervised Learning (2023.acl-long)
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| Challenge: | Current AMR metrics do not consider the entire structure of AMR graphs . |
| Approach: | They propose to learn automatic AMR graph similarity evaluation metric by encoding AMR to a pre-trained language model and using GNN adapters to capture structural information of AMR diagrams. |
| Outcome: | The proposed metric significantly improves the correlations with human semantic scores and remains robust under diverse challenges. |
Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing (2024.lrec-main)
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| Challenge: | Recent studies on AMR parsing often regard this task as a seq2seq translation problem. |
| Approach: | They propose to translate AMR graphs into AMR token sequences in pre-processing and recover AMR from sequences after decoding. |
| Outcome: | The proposed approach outperforms baseline and achieves 85.5 0.1 and 84.2 0.2 Smatch scores on AMR 2.0 and AMR 3.0. |
AnCast++: Document-Level Evaluation of Graph-based Meaning Representations (2025.findings-acl)
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| Challenge: | Abstract Meaning Representation (UMR) is a cross-lingual document-level graph-based representation that extends it to document- level semantic annotations. |
| Approach: | They propose an evaluation metric that unifies evaluation of four distinct sub-structures of UMR. |
| Outcome: | The proposed metric is made available on Github. |
Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation (2025.findings-emnlp)
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| Challenge: | Existing evaluation metrics struggle to evaluate adversarial negative examples . existing metrics struggle in handling adversarials, resulting in low correlations with human judgments. |
| Approach: | They propose a framework that integrates AMR and domain-specific language models for automatic open-domain dialogue evaluation. |
| Outcome: | The proposed evaluation framework achieves strong correlations with human judgments across multiple datasets. |
SWAN: Semantic Watermarking with Abstract Meaning Representation (2026.acl-long)
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Ziping Ye, Gourab Dey, Christos Christodoulopoulos, Charith Peris, Anil Ramakrishna, Weitong Ruan, Aram Galstyan, Kai-Wei Chang, Rahul Gupta, Ninareh Mehrabi
| Challenge: | Existing methods to embed signatures by adjusting token selection preferences during text generation are highly sensitive to paraphrasing and synonyms. |
| Approach: | They propose a framework that embeds watermark signatures into the semantic structure of a sentence using Abstract Meaning Representation (AMR). |
| Outcome: | Empirical evaluation shows SWAN matches state-of-the-art detection performance on unaltered watermarked text while improving robustness against paraphrasing. |