Extra fields

README.md

@@ -65,6 +65,7 @@ dataset_info:
     - [Data Instances](#data-instances)
     - [Data Splits](#data-splits)
     - [Dataset Creation](#dataset-creation)
+    - [Additional Preprocessing](#additional-preprocessing)
   - [Additional Information](#additional-information)
     - [Dataset Curators](#dataset-curators)
     - [Licensing Information](#licensing-information)
@@ -96,19 +97,27 @@ The dataset contains a single default configuration. Dataset examples have the f
 {
   "id": 0,
   "context_en": "Air, water, the continents. So, what is your project about and what are its chances of winning? - Well, my project is awesome. - Oh, good. I took two plants, and I gave them sun and water",
-  "en": "But I gave one special attention to see if <p>it</p> would grow more.",
-  "context_fr": "L'air, l'eau, les continents. Donc, quel est le sujet de ton projet et quelles sont ses chances de gagner ? - Bien, mon projet est impressionnant. - Oh, bien. J'ai pris deux <hon>plantes<hoff> , et je leur ai donné de l'eau et du soleil.",
-  "fr": "Mais j'ai donné une attention particulière à une pour voir si <p>elle</p> grandit plus.",
+  "en": "But I gave one special attention to see if it would grow more.",
+  "context_fr": "L'air, l'eau, les continents. Donc, quel est le sujet de ton projet et quelles sont ses chances de gagner ? - Bien, mon projet est impressionnant. - Oh, bien. J'ai pris deux plantes , et je leur ai donné de l'eau et du soleil.",
+  "fr": "Mais j'ai donné une attention particulière à une pour voir si elle grandit plus.",
+  "contrast_fr": "Mais j'ai donné une attention particulière à une pour voir si il grandit plus.",
+  "context_en_with_tags": "Air, water, the continents. So, what is your project about and what are its chances of winning? - Well, my project is awesome. - Oh, good. I took two plants, and I gave them sun and water",
+  "en_with_tags": "But I gave one special attention to see if <p>it</p> would grow more.",
+  "context_fr_with_tags": "L'air, l'eau, les continents. Donc, quel est le sujet de ton projet et quelles sont ses chances de gagner ? - Bien, mon projet est impressionnant. - Oh, bien. J'ai pris deux <hon>plantes<hoff> , et je leur ai donné de l'eau et du soleil.",
+  "fr_with_tags": "Mais j'ai donné une attention particulière à une pour voir si <p>elle</p> grandit plus.",
+  "contrast_fr_with_tags": "Mais j'ai donné une attention particulière à une pour voir si <p>il</p> grandit plus.",
   "has_supporting_context": true,
 }
 ```
 
-In every example, the pronoun of interest and its translation are surrounded by `<p>...</p>` tags. These are guaranteed to be found in the `en` and `fr` field, respectively.
+In every example, the pronoun of interest and its translation are surrounded by `<p>...</p>` tags. These are guaranteed to be found in the `en_with_tags` and `fr_with_tags` field, respectively.
 
 Any span surrounded by `<hon>...<hoff>` tags was identified by human annotators as supporting context to correctly translate the pronoun of interest. These spans can be missing altogether (i.e. no contextual information needed), or they can be found in any of the available fields. The `has_supporting_context` field indicates whether the example contains any supporting context.
 
 In the example above, the translation of the pronoun `it` (field `en`) is ambiguous, and the correct translation to the feminine French pronoun `elle` (in field `fr`) is only possible thanks to the supporting feminine noun `plantes` in the field `context_fr`. Since the example contains supporting context, the `has_supporting_context` field is set to `true`.
 
+Fields with the `_with_tags` suffix contain tags around pronouns of interest and supporting context, while their counterparts without the suffix contain the same text without tags, to facilitate direct usage with machine translation models.
+
 ### Data Splits
 
 The dataset is split into `train`, `validation` and `test` sets. In the following table, we report the number of examples in the original dataset and in this filtered version in which examples containing malformed tags were removed.
@@ -127,6 +136,16 @@ From the original paper:
 
 Please refer to the original article [Do Context-Aware Translation Models Pay the Right Attention?](https://aclanthology.org/2021.acl-long.65/) for additional information on dataset creation.
 
+### Additional Preprocessing
+
+Compared to the original SCAT corpus, the following differences are present in this version:
+
+- Examples were filtered using the [filter_scat.py](filter_scat.py) script to retain only examples containing well-formed tags, and remove superfluous tags. Superfluous tags are defined as nested `<hon><p>...</p><hoff>` tags that represent lack of contextual information for disambiguating the correct pronoun. In this case, the outer `<hon>...<hoff>` tag was removed.
+
+- Sentences stripped from tags are provided in fields without the `_with_tags` suffix.
+
+- An extra contrastive sentence using the pronoun of interest belonging to the opposite gender is available in the `contrast_fr` field. The swap was performed using a simple lexical heuristic (refer to `swap_pronoun` in [`scat.py`](./scat.py)), and we do not guarantee grammatical correctness of the sentence.
+
 ## Additional Information
 ### Dataset Curators
 

scat.py

@@ -72,7 +72,7 @@ class ScatConfig(datasets.BuilderConfig):
         self.target_language = target_language
 
 
-class WmtVat(datasets.GeneratorBasedBuilder):
+class Scat(datasets.GeneratorBasedBuilder):
 
     VERSION = datasets.Version("1.0.0")
 
@@ -80,6 +80,27 @@ class WmtVat(datasets.GeneratorBasedBuilder):
 
     DEFAULT_CONFIG_NAME = "sentences"
 
+    @staticmethod
+    def clean_string(txt: str):
+        return txt.replace("<p>", "").replace("</p>", "").replace("<hon>", "").replace("<hoff>", "")
+
+    @staticmethod
+    def swap_pronoun(txt: str):
+        pron: str = re.findall(r"<p>([^<]*)</p>", txt)[0]
+        new_pron = pron
+        is_cap = pron.istitle()
+        if pron.lower() == "elles":
+            new_pron = "ils"
+        if pron.lower() == "elle":
+            new_pron = "il"
+        if pron.lower() == "ils":
+            new_pron = "elles"
+        if pron.lower() == "il":
+            new_pron = "elle"
+        if is_cap:
+            new_pron = new_pron.capitalize()
+        return txt.replace(f"<p>{pron}</p>", f"<p>{new_pron}</p>")
+
     def _info(self):
         features = datasets.Features(
             {
@@ -88,6 +109,11 @@ class WmtVat(datasets.GeneratorBasedBuilder):
                 "en": datasets.Value("string"),
                 "context_fr": datasets.Value("string"),
                 "fr": datasets.Value("string"),
+                "contrast_fr": datasets.Value("string"),
+                "context_en_with_tags": datasets.Value("string"),
+                "en_with_tags": datasets.Value("string"),
+                "context_fr_with_tags": datasets.Value("string"),
+                "fr_with_tags": datasets.Value("string"),
                 "has_supporting_context": datasets.Value("bool"),
             }
         )
@@ -139,11 +165,18 @@ class WmtVat(datasets.GeneratorBasedBuilder):
             has_supporting_context = False
             if "<hon>" in allfields and "<hoff>" in allfields:
                 has_supporting_context = True
+            contrast_fr = self.swap_pronoun(f)
             yield i, {
                 "id": i,
-                "context_en": ce,
-                "en": e,
-                "context_fr": cf,
-                "fr": f,
+                "context_en": self.clean_string(ce),
+                "en": self.clean_string(e),
+                "context_fr": self.clean_string(cf),
+                "fr": self.clean_string(f),
+                "contrast_fr": self.clean_string(contrast_fr),
+                "context_en_with_tags": ce,
+                "en_with_tags": e,
+                "context_fr_with_tags": cf,
+                "fr_with_tags": f,
+                "contrast_fr_with_tags": contrast_fr,
                 "has_supporting_context": has_supporting_context,
             }