Update frontend/src/pages/QuotePage/QuotePage.js
Browse files
frontend/src/pages/QuotePage/QuotePage.js
CHANGED
@@ -108,33 +108,33 @@ const priorWork = [
|
|
108 |
// },
|
109 |
];
|
110 |
|
111 |
-
const benchmarks = [
|
112 |
-
|
113 |
-
|
114 |
-
|
115 |
-
|
116 |
-
|
117 |
-
|
118 |
-
|
119 |
-
|
120 |
-
|
121 |
-
|
122 |
-
|
123 |
-
|
124 |
-
|
125 |
-
|
126 |
-
|
127 |
-
|
128 |
-
|
129 |
-
|
130 |
-
|
131 |
-
|
132 |
-
|
133 |
-
|
134 |
-
}`,
|
135 |
-
|
136 |
-
|
137 |
-
];
|
138 |
|
139 |
const CitationBlock = ({ citation, title, authors, url, type }) => {
|
140 |
const handleCopy = () => {
|
|
|
108 |
// },
|
109 |
];
|
110 |
|
111 |
+
// const benchmarks = [
|
112 |
+
// {
|
113 |
+
// title: "MultiFin: Instruction-Following Evaluation",
|
114 |
+
// authors: "Zhou et al.",
|
115 |
+
// citation: `@inproceedings{jorgensen-etal-2023-multifin,
|
116 |
+
// title = "{M}ulti{F}in: A Dataset for Multilingual Financial {NLP}",
|
117 |
+
// author = "J{\o}rgensen, Rasmus and
|
118 |
+
// Brandt, Oliver and
|
119 |
+
// Hartmann, Mareike and
|
120 |
+
// Dai, Xiang and
|
121 |
+
// Igel, Christian and
|
122 |
+
// Elliott, Desmond",
|
123 |
+
// editor = "Vlachos, Andreas and
|
124 |
+
// Augenstein, Isabelle",
|
125 |
+
// booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
|
126 |
+
// month = may,
|
127 |
+
// year = "2023",
|
128 |
+
// address = "Dubrovnik, Croatia",
|
129 |
+
// publisher = "Association for Computational Linguistics",
|
130 |
+
// url = "https://aclanthology.org/2023.findings-eacl.66/",
|
131 |
+
// doi = "10.18653/v1/2023.findings-eacl.66",
|
132 |
+
// pages = "894--909",
|
133 |
+
// abstract = "Financial information is generated and distributed across the world, resulting in a vast amount of domain-specific multilingual data. Multilingual models adapted to the financial domain would ease deployment when an organization needs to work with multiple languages on a regular basis. For the development and evaluation of such models, there is a need for multilingual financial language processing datasets. We describe MultiFin {--} a publicly available financial dataset consisting of real-world article headlines covering 15 languages across different writing systems and language families. The dataset consists of hierarchical label structure providing two classification tasks: multi-label and multi-class. We develop our annotation schema based on a real-world application and annotate our dataset using both {\textquoteleft}label by native-speaker' and {\textquoteleft}translate-then-label' approaches. The evaluation of several popular multilingual models, e.g., mBERT, XLM-R, and mT5, show that although decent accuracy can be achieved in high-resource languages, there is substantial room for improvement in low-resource languages."
|
134 |
+
// }`,
|
135 |
+
// url: "https://aclanthology.org/2023.findings-eacl.66/#:~:text=We%20describe%20MultiFin%20%2D%2D%20a,%2Dlabel%20and%20multi%2Dclass.",
|
136 |
+
// },
|
137 |
+
// ];
|
138 |
|
139 |
const CitationBlock = ({ citation, title, authors, url, type }) => {
|
140 |
const handleCopy = () => {
|