Datasets:

hpprc
/

janli

@@ -126,25 +126,30 @@ An example of looks as follows:
 A version adopting the column names of a typical NLI dataset.
-- `id`: The number of the sentence pair.
-- `premise`: The premise (sentence_A_Ja).
-- `hypothesis`: The hypothesis (sentence_B_Ja).
-- `label`: The correct label for this sentence pair (either `entailment` or `non-entailment`); in the setting described in the paper, non-entailment = neutral + contradiction (entailment_label_Ja).
-- `heuristics`: The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset.
-- `number_of_NPs`: The number of noun phrase in a sentence.
-- `semtag`: The linguistic phenomena tag.
 #### original
 The original version retaining the unaltered column names.
-- `id`: The number of the sentence pair.
-- `sentence_A_Ja`: The premise.
-- `sentence_B_Ja`: The hypothesis.
-- `entailment_label_Ja`: The correct label for this sentence pair (either `entailment` or `non-entailment`); in the setting described in the paper, non-entailment = neutral + contradiction
-- `heuristics`: The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset.
-- `number_of_NPs`: The number of noun phrase in a sentence.
-- `semtag`: The linguistic phenomena tag.
 ### Data Splits
@@ -155,7 +160,6 @@ The original version retaining the unaltered column names.
 | original | 13,680 |            |  720 |
 ### Annotations
 The annotation process for this Japanese NLI dataset involves tagging each pair (P, H) of a premise and hypothesis with a label for structural pattern and linguistic phenomenon.
@@ -164,11 +168,11 @@ Additionally, 11 categories of Japanese linguistic phenomena and constructions a
 For each linguistic phenomenon, a template for the premise sentence P is fixed, and multiple templates for hypothesis sentences H are created.
 In total, 144 templates for (P, H) pairs are produced.
-Each pair of premise and hypothesis sentences is tagged with an entailment label (entailment or non-entailment), a structural pattern, and a linguistic phenomenon label.
 The JaNLI dataset is generated by instantiating each template 100 times, resulting in a total of 14,400 examples.
 The same number of entailment and non-entailment examples are generated for each phenomenon.
-The structural patterns are annotated with the templates for each linguistic phenomenon, and the ratio of entailment and non-entailment examples is not necessarily 1:1 for each pattern.
 The dataset uses a total of 158 words (nouns and verbs), which occur more than 20 times in the JSICK and JSNLI datasets.
@@ -195,4 +199,4 @@ CC BY-SA 4.0
 ### Contributions
-Thanks to [Hitomi Yanaka](https://hitomiyanaka.mystrikingly.com/) and Koji Mineshima for creating this dataset.

 A version adopting the column names of a typical NLI dataset.
+| Name          | Description                                                                                                                                                                               |
+| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| id            | The number of the sentence pair.                                                                                                                                                          |
+| premise       | The premise (sentence_A_Ja).                                                                                                                                                              |
+| hypothesis    | The hypothesis (sentence_B_Ja).                                                                                                                                                           |
+| label         | The correct label for the sentence pair (either `entailment` or `non-entailment`); in the setting described in the paper, non-entailment = neutral + contradiction (entailment_label_Ja). |
+| heuristics    | The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset.                                                            |
+| number_of_NPs | The number of noun phrase in a sentence.                                                                                                                                                  |
+| semtag        | The linguistic phenomena tag.                                                                                                                                                             |
 #### original
 The original version retaining the unaltered column names.
+| Name                | Description                                                                                                                                                         |
+| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| id                  | The number of the sentence pair.                                                                                                                                    |
+| sentence_A_Ja       | The premise.                                                                                                                                                        |
+| sentence_B_Ja       | The hypothesis.                                                                                                                                                     |
+| entailment_label_Ja | The correct label for this sentence pair (either `entailment` or `non-entailment`); in the setting described in the paper, non-entailment = neutral + contradiction |
+| heuristics          | The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset.                                      |
+| number_of_NPs       | The number of noun phrase in a sentence.                                                                                                                            |
+| semtag              | The linguistic phenomena tag.                                                                                                                                       |
 ### Data Splits
 | original | 13,680 |            |  720 |
 ### Annotations
 The annotation process for this Japanese NLI dataset involves tagging each pair (P, H) of a premise and hypothesis with a label for structural pattern and linguistic phenomenon.
 For each linguistic phenomenon, a template for the premise sentence P is fixed, and multiple templates for hypothesis sentences H are created.
 In total, 144 templates for (P, H) pairs are produced.
+Each pair of premise and hypothesis sentences is tagged with an entailment label (`entailment` or `non-entailment`), a structural pattern, and a linguistic phenomenon label.
 The JaNLI dataset is generated by instantiating each template 100 times, resulting in a total of 14,400 examples.
 The same number of entailment and non-entailment examples are generated for each phenomenon.
+The structural patterns are annotated with the templates for each linguistic phenomenon, and the ratio of `entailment` and `non-entailment` examples is not necessarily 1:1 for each pattern.
 The dataset uses a total of 158 words (nouns and verbs), which occur more than 20 times in the JSICK and JSNLI datasets.
 ### Contributions
+Thanks to [Hitomi Yanaka](https://hitomiyanaka.mystrikingly.com/) and [Koji Mineshima](https://abelard.flet.keio.ac.jp/person/minesima/index-j.html) for creating this dataset.