Papers with Abstract Meaning Representations
Deep Learning Approaches to Text Production (N18-6)
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| Challenge: | Text production is a key component of many NLP applications . Claire Gardent is based in France and is pursuing research in text production . |
| Approach: | This tutorial will cover the fundamentals and state-of-the-art research on neural models for text production. |
| Outcome: | This tutorial will cover the fundamentals and the state-of-the-art research on neural models for text production. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
Evaluating Scoped Meaning Representations (L18-1)
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| Challenge: | Semantic parsing offers many opportunities to improve natural language understanding . current research on open-domain semantic parsers focuses on supervised learning methods . |
| Approach: | They propose a semantically annotated parallel corpus for English, German, Italian, and Dutch . they use a matching tool to evaluate scoped meaning representations to match clauses . |
| Outcome: | The proposed method captures the semantics of negation, modals, quantification, and presupposition triggers . it compares scoped meaning representations to gold standard parsers and finds improvements . |
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. |
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. |