Update phrase_retrieval.py

phrase_retrieval.py

@@ -28,14 +28,21 @@ logger = datasets.logging.get_logger(__name__)
 
 
 _CITATION = """\
+@article{pham2022PiC,
+  title={PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search},
+  author={Pham, Thang M and Yoon, Seunghyun and Bui, Trung and Nguyen, Anh},
+  journal={arXiv preprint arXiv:2207.09068},
+  year={2022}
+}
 """
 
 _DESCRIPTION = """\
+Phrase in Context is a curated benchmark for phrase understanding and semantic search, consisting of three tasks of increasing difficulty: Phrase Similarity (PS), Phrase Retrieval (PR) and Phrase Sense Disambiguation (PSD). The datasets are annotated by 13 linguistic experts on Upwork and verified by two groups: ~1000 AMT crowdworkers and another set of 5 linguistic experts. PiC benchmark is distributed under CC-BY-NC 4.0.
 """
 
-_HOMEPAGE = ""
+_HOMEPAGE = "https://phrase-in-context.github.io/"
 
-_LICENSE = "CC-BY-4.0"
+_LICENSE = "CC-BY-NC-4.0"
 
 _URL = "https://auburn.edu/~tmp0038/PiC/"
 _SPLITS = {
@@ -65,12 +72,12 @@ class PhraseRetrieval(datasets.GeneratorBasedBuilder):
     BUILDER_CONFIGS = [
         PRConfig(
             name=_PR_PASS,
-            version=datasets.Version("1.0.0"),
+            version=datasets.Version("1.0.2"),
             description="The PiC Dataset for Phrase Retrieval at short passage level (~11 sentences)"
         ),
         PRConfig(
             name=_PR_PAGE,
-            version=datasets.Version("1.0.0"),
+            version=datasets.Version("1.0.2"),
             description="The PiC Dataset for Phrase Retrieval at Wiki page level"
         ),
     ]
@@ -83,13 +90,13 @@ class PhraseRetrieval(datasets.GeneratorBasedBuilder):
                     "id": datasets.Value("string"),
                     "title": datasets.Value("string"),
                     "context": datasets.Value("string"),
-                    "query": datasets.Value("string"),
+                    "question": datasets.Value("string"),
                     "answers": datasets.Sequence(
                         {
                             "text": datasets.Value("string"),
                             "answer_start": datasets.Value("int32"),
                         }
-                    ),
+                    )
                 }
             ),
             # No default supervised_keys (as we have to pass both question and context as input).
@@ -105,7 +112,6 @@ class PhraseRetrieval(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-
         urls_to_download = {
             "train": os.path.join(_URL, self.config.name, _SPLITS["train"]),
             "dev": os.path.join(_URL, self.config.name, _SPLITS["dev"]),
@@ -126,20 +132,23 @@ class PhraseRetrieval(datasets.GeneratorBasedBuilder):
         with open(filepath, encoding="utf-8") as f:
             pic_pr = json.load(f)
             for example in pic_pr["data"]:
+                title = example.get("title", "")
+
                 answer_starts = [answer["answer_start"] for answer in example["answers"]]
                 answers = [answer["text"] for answer in example["answers"]]
 
                 # Features currently used are "context", "question", and "answers".
                 # Others are extracted here for the ease of future expansions.
                 yield key, {
-                    "title": example["title"],
+                    "title": title,
                     "context": example["context"],
-                    "query": example["question"],
+                    "question": example["question"],
                     "id": example["id"],
                     "answers": {
                         "answer_start": answer_starts,
                         "text": answers,
-                    },
+                    }
                 }
                 key += 1
 
+