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Found 23 papers, 10 papers with code
Explainable Unsupervised Argument Similarity Rating with Abstract Meaning Representation and Conclusion Generation
When assessing the similarity of arguments, researchers typically use approaches that do not provide interpretable evidence or justifications for their ratings.
Overview of the 2022 Validity and Novelty Prediction Shared Task
This paper provides an overview of the Argument Validity and Novelty Prediction Shared Task that was organized as part of the 9th Workshop on Argument Mining (ArgMining 2022).
Ranked #1 on
ValNov
on ValNov Subtask A
Data Augmentation for Improving the Prediction of Validity and Novelty of Argumentative Conclusions
Using only training data retrieved from related datasets by automatically labeling them for validity and novelty, combined with synthetic data, outperforms the baseline by 11. 5 points in F_1-score.
A Dynamic, Interpreted CheckList for Meaning-oriented NLG Metric Evaluation – through the Lens of Semantic Similarity Rating
Evaluating the quality of generated text is difficult, since traditional NLG evaluation metrics, focusing more on surface form than meaning, often fail to assign appropriate scores. This is especially problematic for AMR-to-text evaluation, given the abstract nature of AMR. Our work aims to support the development and improvement of NLG evaluation metrics that focus on meaning by developing a dynamic CheckList for NLG metrics that is interpreted by being organized around meaning-relevant linguistic phenomena.
Better Smatch = Better Parser? AMR evaluation is not so simple anymore
Recently, astonishing advances have been observed in AMR parsing, as measured by the structural Smatch metric.
SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features
Models based on large-pretrained language models, such as S(entence)BERT, provide effective and efficient sentence embeddings that show high correlation to human similarity ratings, but lack interpretability.
A Dynamic, Interpreted CheckList for Meaning-oriented NLG Metric Evaluation -- through the Lens of Semantic Similarity Rating
Our work aims to support the development and improvement of NLG evaluation metrics that focus on meaning, by developing a dynamic CheckList for NLG metrics that is interpreted by being organized around meaning-relevant linguistic phenomena.
SMARAGD: Synthesized sMatch for Accurate and Rapid AMR Graph Distance
The semantic similarity of graph-based meaning representations, such as Abstract Meaning Representation (AMR), is typically assessed using graph matching algorithms, such as SMATCH (Cai and Knight, 2013).
Weisfeiler-Leman in the BAMBOO: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity
In this work we propose new Weisfeiler-Leman AMR similarity metrics that unify the strengths of previous metrics, while mitigating their weaknesses.
Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing
In cross-lingual Abstract Meaning Representation (AMR) parsing, researchers develop models that project sentences from various languages onto their AMRs to capture their essential semantic structures: given a sentence in any language, we aim to capture its core semantic content through concepts connected by manifold types of semantic relations.
Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMR
Systems that generate natural language text from abstract meaning representations such as AMR are typically evaluated using automatic surface matching metrics that compare the generated texts to reference texts from which the input meaning representations were constructed.
AMR Quality Rating with a Lightweight CNN
Structured semantic sentence representations such as Abstract Meaning Representations (AMRs) are potentially useful in various NLP tasks.
AMR Similarity Metrics from Principles
Different metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs.
Macro F1 and Macro F1
In this note, we show that only under rare circumstances the two computations can be considered equivalent.
Argumentative Relation Classification as Plausibility Ranking
We formulate argumentative relation classification (support vs. attack) as a text-plausibility ranking task.
Dissecting Content and Context in Argumentative Relation Analysis
When assessing relations between argumentative units (e. g., support or attack), computational systems often exploit disclosing indicators or markers that are not part of elementary argumentative units (EAUs) themselves, but are gained from their context (position in paragraph, preceding tokens, etc.).
Automatic Accuracy Prediction for AMR Parsing
Secondly, we perform parse selection based on predicted parse accuracies of candidate parses from alternative systems, with the aim of improving overall results.
An Argument-Marker Model for Syntax-Agnostic Proto-Role Labeling
Semantic proto-role labeling (SPRL) is an alternative to semantic role labeling (SRL) that moves beyond a categorical definition of roles, following Dowty's feature-based view of proto-roles.
Induction of a Large-Scale Knowledge Graph from the Regesta Imperii
We induce and visualize a Knowledge Graph over the Regesta Imperii (RI), an important large-scale resource for medieval history research.
Addressing the Winograd Schema Challenge as a Sequence Ranking Task
The Winograd Schema Challenge targets pronominal anaphora resolution problems which require the application of cognitive inference in combination with world knowledge.
A Mention-Ranking Model for Abstract Anaphora Resolution
We found model variants that outperform the baselines for nominal anaphors, without training on individual anaphor data, but still lag behind for pronominal anaphors.
Ranked #1 on
Abstract Anaphora Resolution
on The ARRAU Corpus
