{"total":22,"rows":[{"id":"2102.01454","title":"MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers","created":"2021-02-02","cited_by_count":470,"area":null},{"id":"2102.01672","title":"The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics","created":"2021-02-02","cited_by_count":239,"area":null},{"id":"2103.09120","title":"Structural Adapters in Pretrained Language Models for AMR-to-text Generation","created":"2021-03-16","cited_by_count":48,"area":null},{"id":"2212.14578","title":"MAUVE Scores for Generative Models: Theory and Practice","created":"2022-12-30","cited_by_count":36,"area":null},{"id":"2206.07023","title":"SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features","created":"2022-06-14","cited_by_count":33,"area":null},{"id":"2104.07555","title":"Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation","created":"2021-04-15","cited_by_count":21,"area":null},{"id":"2305.06993","title":"SMATCH++: Standardized and Extended Evaluation of Semantic Graphs","created":"2023-05-11","cited_by_count":21,"area":null},{"id":"2108.02859","title":"Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization","created":"2021-08-05","cited_by_count":16,"area":null},{"id":"2108.11949","title":"Weisfeiler-Leman in the BAMBOO: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity","created":"2021-08-26","cited_by_count":16,"area":null},{"id":"2210.06461","title":"Better Smatch = Better Parser? AMR evaluation is not so simple anymore","created":"2022-10-12","cited_by_count":9,"area":null},{"id":"2405.05966","title":"Natural Language Processing RELIES on Linguistics","created":"2024-05-09","cited_by_count":9,"area":null},{"id":"2205.12176","title":"A Dynamic, Interpreted CheckList for Meaning-oriented NLG Metric Evaluation -- through the Lens of Semantic Similarity Rating","created":"2022-05-24","cited_by_count":5,"area":null},{"id":"2405.19094","title":"Faithful Chart Summarization with ChaTS-Pi","created":"2024-05-29","cited_by_count":5,"area":null},{"id":"2402.08496","title":"A Systematic Review of Data-to-Text NLG","created":"2024-02-13","cited_by_count":4,"area":null},{"id":"2110.01176","title":"Contextualized Semantic Distance between Highly Overlapped Texts","created":"2021-10-04","cited_by_count":2,"area":null},{"id":"2209.07258","title":"Graph-to-Text Generation with Dynamic Structure Pruning","created":"2022-09-15","cited_by_count":2,"area":null},{"id":"2302.05900","title":"Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters","created":"2023-02-12","cited_by_count":2,"area":null},{"id":"2110.15931","title":"Unsupervised Full Constituency Parsing with Neighboring Distribution Divergence","created":"2021-10-29","cited_by_count":0,"area":null},{"id":"2203.13226","title":"SMARAGD: Learning SMatch for Accurate and Rapid Approximate Graph Distance","created":"2022-03-24","cited_by_count":0,"area":null},{"id":"2206.07328","title":"A Survey : Neural Networks for AMR-to-Text","created":"2022-06-15","cited_by_count":0,"area":null},{"id":"2303.01555","title":"Counterfactual Edits for Generative Evaluation","created":"2023-03-02","cited_by_count":0,"area":null},{"id":"2306.00936","title":"AMR4NLI: Interpretable and robust NLI measures from semantic graphs","created":"2023-06-01","cited_by_count":0,"area":null}],"next_cursor":null}