A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing (2021.emnlp-main)
Copied to clipboard
| 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. |
Similar Papers
AMR Parsing as Graph Prediction with Latent Alignment (P18-1)
Copied to clipboard
| Challenge: | Abstract meaning representations (AMRs) are sentence-level semantic representations . lack of explicit alignments between nodes in graphs and words in sentences is a challenge . |
| Approach: | They propose a neural parser which treats alignments as latent variables within a joint probabilistic model of concepts, relations and alignments. |
| Outcome: | The proposed parser achieves the best reported results on the standard benchmark (74.4% on LDC2016E25). |
Graph Pre-training for AMR Parsing and Generation (2022.acl-long)
Copied to clipboard
| 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. |
Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments (2021.acl-long)
Copied to clipboard
| 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. |
Semantically Inspired AMR Alignment for the Portuguese Language (2020.emnlp-main)
Copied to clipboard
| 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. |
Factorising AMR generation through syntax (N19-1)
Copied to clipboard
| 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. |
A Structured Syntax-Semantics Interface for English-AMR Alignment (N18-1)
Copied to clipboard
| 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. |
AMR Parsing with Latent Structural Information (2020.acl-main)
Copied to clipboard
| 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. |
Cross-lingual AMR Aligner: Paying Attention to Cross-Attention (2023.findings-acl)
Copied to clipboard
| 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. |
A Survey of AMR Applications (2024.emnlp-main)
Copied to clipboard
| 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. |
Cross-domain Generalization for AMR Parsing (2022.emnlp-main)
Copied to clipboard
| 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. |