Inflate dataset

.gitattributes

@@ -53,3 +53,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+train.json filter=lfs diff=lfs merge=lfs -text
+dev.json filter=lfs diff=lfs merge=lfs -text
+test.json filter=lfs diff=lfs merge=lfs -text
+challenge.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,12 @@
 ---
 license: cc-by-sa-4.0
+task_categories:
+  - conversational
+  - question-answering
+  - text-generation
+tags:
+- qa
+- knowledge-graph
+language:
+- en
 ---

challenge.json

@@ -0,0 +1,3 @@
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+oid sha256:dd2f4fc1e61d2e9ef9a059bb15a4cc6ef1c1c68534ee80474be53a6cf6026f4b
+size 968686

dev.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0518c960195c31ad995fb0b700cb0b84ed4f6717365a1416c7c1ee143928300f
+size 10345623

test.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1390503972269520e74edc2f3489114c488b05ab3310be36cc2e3f0ead92ccd4
+size 11856922

train.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f77a46ef327cf8f0a56cefd1bbd170bc64551c7fbba10db9e289e26f1f80acd
+size 82137230

webnlgqa.py

@@ -0,0 +1,199 @@
+import os
+import zipfile
+import json
+import base64
+import sys
+import traceback
+
+import datasets
+
+_CITATION = """\
+    @inproceedings{lecorve2022sparql2text,
+        title={SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications},
+        author={Lecorv\'e, Gw\'enol\'e and Veyret, Morgan and Brabant, Quentin and Rojas-Barahona, Lina M.},
+        journal={Proceedings of the Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP)},
+        year={2022}
+    }
+"""
+
+_HOMEPAGE = ""
+
+_URLS = {
+    "train": "train.json",
+    "dev": "dev.json",
+    "test": "test.json",
+    "challenge": "challenge.json"
+}
+
+_DESCRIPTION = """\
+Augmented version of WebNLG v3.0 English with follow-up SPARQL queries with their associated answer(s). A small portion of it also contains natural language questions associated with the queries.
+"""
+
+class WebNLGQA(datasets.GeneratorBasedBuilder):
+    """
+    WebNLG-QA: Augmented version of WebNLG v3.0 English with follow-up SPARQL queries with their associated answer(s). A small portion of it also contains natural language questions associated with the queries.
+    """
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {
+                    "category": datasets.Value("string"),
+                    "size": datasets.Value("int32"),
+                    "id": datasets.Value("string"),
+                    "eid": datasets.Value("string"),
+                    "original_triple_sets": [
+                        {"subject": datasets.Value("string"),
+                         "property": datasets.Value("string"),
+                         "object": datasets.Value("string")}
+                    ],
+                    "modified_triple_sets": [
+                        {"subject": datasets.Value("string"),
+                         "property": datasets.Value("string"),
+                         "object": datasets.Value("string")}
+                    ],
+                    "shape": datasets.Value("string"),
+                    "shape_type": datasets.Value("string"),
+                    "lex": datasets.Sequence(
+                        {
+                            "comment": datasets.Value("string"),
+                            "lid": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                            "lang": datasets.Value("string"),
+                        }
+                    ),
+                    "test_category": datasets.Value("string"),
+                    "dbpedia_links": datasets.Sequence(datasets.Value("string")),
+                    "links": datasets.Sequence(datasets.Value("string")),
+                    "graph": [
+                        [datasets.Value("string")]
+                    ],
+                    "main_entity": datasets.Value("string"),
+                    "mappings": [
+                                    {
+                                        "modified": datasets.Value("string"),
+                                        "readable": datasets.Value("string"),
+                                        "graph": datasets.Value("string")
+                                    }
+                                ],
+                    "dialogue": [
+                            {
+                                "question": [ {
+                                  "source": datasets.Value("string"),
+                                  "text": datasets.Value("string")
+                                }],
+                                "graph_query": datasets.Value("string"),
+                                "readable_query": datasets.Value("string"),
+                                "graph_answer": [
+                                    datasets.Value("string")
+                                ],
+                                "readable_answer": [
+                                    datasets.Value("string")
+                                ],
+                                "type": [ datasets.Value("string") ]
+                            }
+                        ]
+                }
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        paths = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": paths['train'],
+                            "split": "train"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": paths['dev'],
+                            "split": "dev"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": paths['test'],
+                            "split": "test"}
+            ),
+            datasets.SplitGenerator(
+                name="challenge",
+                gen_kwargs={"filepath": paths['challenge'],
+                            "split": "challenge"}
+            )
+        ]
+
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+
+        def transform_sample(original_sample):
+            transformed_sample = {
+                    "category": "",
+                    "size": -1,
+                    "id": "",
+                    "eid": "",
+                    "original_triple_sets": [],
+                    "modified_triple_sets": [],
+                    "shape": "",
+                    "shape_type": "",
+                    "lex": [],
+                    "test_category": "",
+                    "dbpedia_links": [],
+                    "links": [],
+                    "graph": [],
+                    "main_entity": "",
+                    "mappings": [],
+                    "dialogue": []
+                }
+
+            for (old_key, new_key) in [("modifiedtripleset", "modified_triple_sets"), ("originaltriplesets", "original_triple_sets"), ("dbpedialinks", "dbpedia_links"), ("lexicalisations", "lex"), ("xml_id", "eid")]:
+                original_sample[new_key] = original_sample[old_key]
+                del original_sample[old_key]
+
+            original_sample["original_triple_sets"] = original_sample["original_triple_sets"]["originaltripleset"][0]
+
+            for l in original_sample["lex"]:
+                l["lid"] = l["xml_id"]
+                del l["xml_id"]
+                l["text"] = l["lex"]
+                del l["lex"]
+
+            for turn in original_sample["dialogue"]:
+                if "question" in turn:
+                    old_format = turn["question"]
+                    new_format = []
+                    for source, text in old_format.items():
+                        new_format.append({"source": source, "text": text})
+                    turn["question"] = new_format
+
+
+            for k in transformed_sample:
+                if k in original_sample:
+                    transformed_sample[k] = original_sample[k]
+            # transformed_sample.update(original_sample)
+
+            return transformed_sample
+
+        # Yields (key, example) tuples from the dataset
+        with open(filepath,'r') as f:
+            data = json.load(f)
+            key = 0
+            for it in data:
+                yield key, transform_sample(it)
+                key += 1