Update README.md

README.md

@@ -61,8 +61,8 @@ Description of the dataset columns:
 | lang                     | str         | The language of the data instance                                                                                |
 | question                | str         | The question to answer                                                                                           |
 | context           | str         | The context, a Wikipedia paragraph that might or might not contain the answer to the question                    | 
-| is_impossible | bool | FALSE if the question can be answered given the context, TRUE otherwise |
-| answer_start  | int   | The character index in 'context' where the answer starts. If the question is unanswerable, this is -1  |
+| answertable | bool | True if the question can be answered given the context, False otherwise |
+| answer_start  | int   | The character index in 'context' where the answer starts. If the question is unanswerable given the context, this is -1  |
 | answer   | str   | The answer, a span of text from 'context'. If the question is unanswerable given the context, this can be 'yes' or 'no'            |
 
 
@@ -76,22 +76,82 @@ Check out the [datasets ducumentations](https://huggingface.co/docs/datasets/qui
 
 `dataset.to_pandas`, to convert the dataset into a pandas.DataFrame format.
 
-
+## Citations
 ```
-@inproceedings{xorqa,
-    title   = {{XOR} {QA}: Cross-lingual Open-Retrieval Question Answering},
-    author  = {Akari Asai and Jungo Kasai and Jonathan H. Clark and Kenton Lee and Eunsol Choi and Hannaneh Hajishirzi},
-    booktitle={NAACL-HLT},
-    year    = {2021}
+@article{clark-etal-2020-tydi,
+    title = "{T}y{D}i {QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages",
+    author = "Clark, Jonathan H.  and
+      Choi, Eunsol  and
+      Collins, Michael  and
+      Garrette, Dan  and
+      Kwiatkowski, Tom  and
+      Nikolaev, Vitaly  and
+      Palomaki, Jennimaria",
+    editor = "Johnson, Mark  and
+      Roark, Brian  and
+      Nenkova, Ani",
+    journal = "Transactions of the Association for Computational Linguistics",
+    volume = "8",
+    year = "2020",
+    address = "Cambridge, MA",
+    publisher = "MIT Press",
+    url = "https://aclanthology.org/2020.tacl-1.30",
+    doi = "10.1162/tacl_a_00317",
+    pages = "454--470",
+    abstract = "Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations. We present TyDi QA{---}a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology{---}the set of linguistic features each language expresses{---}such that we expect models performing well on this set to generalize across a large number of the world{'}s languages. We present a quantitative analysis of the data quality and example-level qualitative linguistic analyses of observed language phenomena that would not be found in English-only corpora. To provide a realistic information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but don{'}t know the answer yet, and the data is collected directly in each language without the use of translation.",
 }
-```
 
-```
-@article{tydiqa,
-title   = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
-author  = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
-year    = {2020},
-journal = {Transactions of the Association for Computational Linguistics}
+@inproceedings{asai-etal-2021-xor,
+    title = "{XOR} {QA}: Cross-lingual Open-Retrieval Question Answering",
+    author = "Asai, Akari  and
+      Kasai, Jungo  and
+      Clark, Jonathan  and
+      Lee, Kenton  and
+      Choi, Eunsol  and
+      Hajishirzi, Hannaneh",
+    editor = "Toutanova, Kristina  and
+      Rumshisky, Anna  and
+      Zettlemoyer, Luke  and
+      Hakkani-Tur, Dilek  and
+      Beltagy, Iz  and
+      Bethard, Steven  and
+      Cotterell, Ryan  and
+      Chakraborty, Tanmoy  and
+      Zhou, Yichao",
+    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
+    month = jun,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.naacl-main.46",
+    doi = "10.18653/v1/2021.naacl-main.46",
+    pages = "547--564",
+    abstract = "Multilingual question answering tasks typically assume that answers exist in the same language as the question. Yet in practice, many languages face both information scarcity{---}where languages have few reference articles{---}and information asymmetry{---}where questions reference concepts from other cultures. This work extends open-retrieval question answering to a cross-lingual setting enabling questions from one language to be answered via answer content from another language. We construct a large-scale dataset built on 40K information-seeking questions across 7 diverse non-English languages that TyDi QA could not find same-language answers for. Based on this dataset, we introduce a task framework, called Cross-lingual Open-Retrieval Question Answering (XOR QA), that consists of three new tasks involving cross-lingual document retrieval from multilingual and English resources. We establish baselines with state-of-the-art machine translation systems and cross-lingual pretrained models. Experimental results suggest that XOR QA is a challenging task that will facilitate the development of novel techniques for multilingual question answering. Our data and code are available at \url{https://nlp.cs.washington.edu/xorqa/}.",
+}
+
+@inproceedings{muller-etal-2023-evaluating,
+    title = "Evaluating and Modeling Attribution for Cross-Lingual Question Answering",
+    author = "Muller, Benjamin  and
+      Wieting, John  and
+      Clark, Jonathan  and
+      Kwiatkowski, Tom  and
+      Ruder, Sebastian  and
+      Soares, Livio  and
+      Aharoni, Roee  and
+      Herzig, Jonathan  and
+      Wang, Xinyi",
+    editor = "Bouamor, Houda  and
+      Pino, Juan  and
+      Bali, Kalika",
+    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.emnlp-main.10",
+    doi = "10.18653/v1/2023.emnlp-main.10",
+    pages = "144--157",
+    abstract = "Trustworthy answer content is abundant in many high-resource languages and is instantly accessible through question answering systems {---} yet this content can be hard to access for those that do not speak these languages. The leap forward in cross-lingual modeling quality offered by generative language models offers much promise, yet their raw generations often fall short in factuality. To improve trustworthiness in these systems, a promising direction is to attribute the answer to a retrieved source, possibly in a content-rich language different from the query. Our work is the first to study attribution for cross-lingual question answering. First, we collect data in 5 languages to assess the attribution level of a state-of-the-art cross-lingual QA system. To our surprise, we find that a substantial portion of the answers is not attributable to any retrieved passages (up to 50{\%} of answers exactly matching a gold reference) despite the system being able to attend directly to the retrieved text. Second, to address this poor attribution level, we experiment with a wide range of attribution detection techniques. We find that Natural Language Inference models and PaLM 2 fine-tuned on a very small amount of attribution data can accurately detect attribution. With these models, we improve the attribution level of a cross-lingual QA system. Overall, we show that current academic generative cross-lingual QA systems have substantial shortcomings in attribution and we build tooling to mitigate these issues.",
 }
 
 ```