Datasets:

GEM
/

web_nlg

@@ -565,7 +565,7 @@ We evaluated a wide range of models as part of the GEM benchmark.
 <!-- info: What are the most relevant previous results for this task/dataset? -->
 <!-- scope: microscope -->
-Results can be found at https://gem-benchmark.com/results.

 <!-- info: What are the most relevant previous results for this task/dataset? -->
 <!-- scope: microscope -->
+Results can be found on the [GEM website](https://gem-benchmark.com/results).

web_nlg.json CHANGED Viewed

@@ -4,7 +4,7 @@
       "other-metrics-definitions": "N/A",
       "has-previous-results": "yes",
       "current-evaluation": "We evaluated a wide range of models as part of the GEM benchmark.",
-      "previous-results": "Results can be found at https://gem-benchmark.com/results.",
       "original-evaluation": "For both languages, the participating systems are automatically evaluated in a multi-reference scenario. Each English hypothesis is compared to a maximum of 5 references, and each Russian one to a maximum of 7 references. On average, English data has 2.89 references per test instance, and Russian data has 2.52 references per instance. \n\nIn a human evaluation, example are uniformly sampled across size of triple sets and the following dimensions are assessed (on MTurk and Yandex.Toloka):\n\n1. Data Coverage: Does the text include descriptions of all predicates presented in the data?\n2. Relevance: Does the text describe only such predicates (with related subjects and objects), which are found in the data?\n3. Correctness: When describing predicates which are found in the data, does the text mention correct the objects and adequately introduces the subject for this specific predicate?\n4. Text Structure: Is the text grammatical, well-structured, written in acceptable English language?\n5. Fluency: Is it possible to say that the text progresses naturally, forms a coherent whole and it is easy to understand the text?\n\nFor additional information like the instructions, we refer to the original paper.\n"
     }
   },

       "other-metrics-definitions": "N/A",
       "has-previous-results": "yes",
       "current-evaluation": "We evaluated a wide range of models as part of the GEM benchmark.",
+      "previous-results": "Results can be found on the [GEM website](https://gem-benchmark.com/results).",
       "original-evaluation": "For both languages, the participating systems are automatically evaluated in a multi-reference scenario. Each English hypothesis is compared to a maximum of 5 references, and each Russian one to a maximum of 7 references. On average, English data has 2.89 references per test instance, and Russian data has 2.52 references per instance. \n\nIn a human evaluation, example are uniformly sampled across size of triple sets and the following dimensions are assessed (on MTurk and Yandex.Toloka):\n\n1. Data Coverage: Does the text include descriptions of all predicates presented in the data?\n2. Relevance: Does the text describe only such predicates (with related subjects and objects), which are found in the data?\n3. Correctness: When describing predicates which are found in the data, does the text mention correct the objects and adequately introduces the subject for this specific predicate?\n4. Text Structure: Is the text grammatical, well-structured, written in acceptable English language?\n5. Fluency: Is it possible to say that the text progresses naturally, forms a coherent whole and it is easy to understand the text?\n\nFor additional information like the instructions, we refer to the original paper.\n"
     }
   },