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Small fixes in README

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  1. README.md +4 -4
README.md CHANGED
@@ -13,7 +13,7 @@ widget:
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  - text: "He ate a sweet apple. What is the definition of apple?"
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  example_title: "Definition generation"
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  - text: "The paper contains a number of original ideas about color perception. What is the definition of original?"
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- example_title: "Definiton generation"
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  license: cc-by-sa-4.0
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  datasets:
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  - marksverdhei/wordnet-definitions-en-2021
@@ -34,7 +34,7 @@ See details in the paper `Interpretable Word Sense Representations via Definitio
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  The model is intended for research purposes, as a source of contextualized dictionary-like lexical definitions.
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- The fine-tuning datasets were limited to English
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  Although the original FLAN-T5 is a multilingual model, we did not thoroughly evaluate its ability to generate definitions in languages other than English.
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  Generated definitions can contain all sorts of biases and stereotypes, stemming from the underlying language model.
@@ -47,12 +47,12 @@ Three datasets were used to fine-tune the model:
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  - *Oxford dictionary or CHA* ([Gadetsky et al., ACL 2018](https://aclanthology.org/P18-2043/))
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  - English subset of *CodWoE* ([Mickus et al., SemEval 2022](https://aclanthology.org/2022.semeval-1.1/))
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- FLAN-T5-Definition XL achieves the following results on the WordNet test set ([Ishiwatari et al., NAACL 2019](https://aclanthology.org/N19-1350/)):
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  - ROUGE-L: 52.21
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  - BLEU: 32.81
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  - BERT-F1: 92.16
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- FLAN-T5-Definition XL achieves the following results on the Oxford dictionary test set ([Gadetsky et al., ACL 2018](https://aclanthology.org/P18-2043/)):
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  - ROUGE-L: 38.72
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  - BLEU: 18.69
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  - BERT-F1: 89.75
 
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  - text: "He ate a sweet apple. What is the definition of apple?"
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  example_title: "Definition generation"
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  - text: "The paper contains a number of original ideas about color perception. What is the definition of original?"
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+ example_title: "Definition generation"
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  license: cc-by-sa-4.0
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  datasets:
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  - marksverdhei/wordnet-definitions-en-2021
 
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  The model is intended for research purposes, as a source of contextualized dictionary-like lexical definitions.
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+ The fine-tuning datasets were limited to English.
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  Although the original FLAN-T5 is a multilingual model, we did not thoroughly evaluate its ability to generate definitions in languages other than English.
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  Generated definitions can contain all sorts of biases and stereotypes, stemming from the underlying language model.
 
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  - *Oxford dictionary or CHA* ([Gadetsky et al., ACL 2018](https://aclanthology.org/P18-2043/))
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  - English subset of *CodWoE* ([Mickus et al., SemEval 2022](https://aclanthology.org/2022.semeval-1.1/))
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+ FLAN-T5-Definition XL achieves the following results on the WordNet test set:
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  - ROUGE-L: 52.21
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  - BLEU: 32.81
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  - BERT-F1: 92.16
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+ FLAN-T5-Definition XL achieves the following results on the Oxford dictionary test set:
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  - ROUGE-L: 38.72
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  - BLEU: 18.69
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  - BERT-F1: 89.75