Update metadata

README.md

@@ -103,10 +103,9 @@ dataset_info:
 
 ## Dataset Description
 
-- **Homepage:** [NarrativeQA Homepage](https://deepmind.com/research/open-source/narrativeqa)
-- **Repository:** [NarrativeQA Repo](https://github.com/deepmind/narrativeqa)
-- **Paper:** [The NarrativeQA Reading Comprehension Challenge](https://arxiv.org/pdf/1712.07040.pdf)
-- **Leaderboard:**
+- **Repository:** https://github.com/deepmind/narrativeqa
+- **Paper:** https://arxiv.org/abs/1712.07040
+- **Paper:** https://aclanthology.org/Q18-1023/
 - **Point of Contact:** [Tomáš Kočiský](mailto:tkocisky@google.com) [Jonathan Schwarz](mailto:schwarzjn@google.com) [Phil Blunsom](pblunsom@google.com) [Chris Dyer](cdyer@google.com) [Karl Moritz Hermann](mailto:kmh@google.com) [Gábor Melis](mailto:melisgl@google.com) [Edward Grefenstette](mailto:etg@google.com)
 
 ### Dataset Summary
@@ -237,16 +236,28 @@ The dataset is released under a [Apache-2.0 License](https://github.com/deepmind
 ### Citation Information
 
 ```
-@article{narrativeqa,
-author = {Tom\'a\v s Ko\v cisk\'y and Jonathan Schwarz and Phil Blunsom and
-          Chris Dyer and Karl Moritz Hermann and G\'abor Melis and
-          Edward Grefenstette},
-title = {The {NarrativeQA} Reading Comprehension Challenge},
-journal = {Transactions of the Association for Computational Linguistics},
-url = {https://TBD},
-volume = {TBD},
-year = {2018},
-pages = {TBD},
+@article{kocisky-etal-2018-narrativeqa,
+    title = "The {N}arrative{QA} Reading Comprehension Challenge",
+    author = "Ko{\v{c}}isk{\'y}, Tom{\'a}{\v{s}}  and
+      Schwarz, Jonathan  and
+      Blunsom, Phil  and
+      Dyer, Chris  and
+      Hermann, Karl Moritz  and
+      Melis, G{\'a}bor  and
+      Grefenstette, Edward",
+    editor = "Lee, Lillian  and
+      Johnson, Mark  and
+      Toutanova, Kristina  and
+      Roark, Brian",
+    journal = "Transactions of the Association for Computational Linguistics",
+    volume = "6",
+    year = "2018",
+    address = "Cambridge, MA",
+    publisher = "MIT Press",
+    url = "https://aclanthology.org/Q18-1023",
+    doi = "10.1162/tacl_a_00023",
+    pages = "317--328",
+    abstract = "Reading comprehension (RC){---}in contrast to information retrieval{---}requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.",
 }
 ```
 

narrativeqa.py

@@ -22,16 +22,28 @@ import datasets
 
 
 _CITATION = """\
-@article{narrativeqa,
-author = {Tom\\'a\\v s Ko\\v cisk\\'y and Jonathan Schwarz and Phil Blunsom and
-          Chris Dyer and Karl Moritz Hermann and G\\'abor Melis and
-          Edward Grefenstette},
-title = {The {NarrativeQA} Reading Comprehension Challenge},
-journal = {Transactions of the Association for Computational Linguistics},
-url = {https://TBD},
-volume = {TBD},
-year = {2018},
-pages = {TBD},
+@article{kocisky-etal-2018-narrativeqa,
+    title = "The {N}arrative{QA} Reading Comprehension Challenge",
+    author = "Ko{\v{c}}isk{\'y}, Tom{\'a}{\v{s}}  and
+      Schwarz, Jonathan  and
+      Blunsom, Phil  and
+      Dyer, Chris  and
+      Hermann, Karl Moritz  and
+      Melis, G{\'a}bor  and
+      Grefenstette, Edward",
+    editor = "Lee, Lillian  and
+      Johnson, Mark  and
+      Toutanova, Kristina  and
+      Roark, Brian",
+    journal = "Transactions of the Association for Computational Linguistics",
+    volume = "6",
+    year = "2018",
+    address = "Cambridge, MA",
+    publisher = "MIT Press",
+    url = "https://aclanthology.org/Q18-1023",
+    doi = "10.1162/tacl_a_00023",
+    pages = "317--328",
+    abstract = "Reading comprehension (RC){---}in contrast to information retrieval{---}requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.",
 }
 """