Update README.md

README.md

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 We present a collection of Winograd Schemas in the Thai language. These schemas are adapted from the original set of English Winograd Schemas proposed by [Levesque et al.](https://commonsensereasoning.org/2011/papers/Levesque.pdf), which was based on [Ernest Davis's collection](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WSCollection.xml).
 
 ## Dataset Translation
-We hired two professional translators who are native Thai speakers and fluent in English with experience in translating from English to Thai.  The translation work was divided into an 85:200 ratio, ensuring that neither translator had an excessive workload. We provided translation guidelines to the translators, instructing them to adapt names and contexts to suit the Thai language while preserving the ambiguity and nuances of the original schema. We also asked the translators to mark any translated names and translations they were unsure about, so that the validator in the next step could pay extra attention to those instances. In adapting the schemas to the Thai language, names like Paul and George have been changed to Thai names such as Mana and Piti, respectively. This is an instance of adapting names to better suit the Thai context while preserving the essence of the original content.
+Two professional translators, who were native Thai speakers fluent in English and had experience translating from English to Thai, were hired. In a pilot translation phase, one native speaker translated the first 85 sentences. Based on a qualitative analysis of these 85 sentences, guidelines were provided for a second native speaker to translate the remaining 200 sentences. In total, 285 sentences were translated from English to Thai. Translation guidelines were provided, instructing them to adapt names and contexts to suit the Thai language while preserving the ambiguity and nuances of the original schema. The translators were also asked to mark any translated names and translations they were unsure about, so that the validator in the next step could pay extra attention to those instances. For example, in \autoref{fig:example}, the names Paul and George were changed to Mana and Piti, respectively, adapting the names to better suit the Thai context while preserving the essence of the original content.
 
 ## Dataset Validation
-We reviewed the translated Winograd Schemas with two native Thai speakers and made final adjustments to ensure clarity. A validator was provided with the translations and tasked with identifying any potential issues. They were instructed to pay closer attention to the text marked by the translators, which included changes to names and translations that the translators were unsure about. Based on the validator's feedback, we made some final adjustments to the translations. Furthermore, we have made the dataset publicly available, inviting other native Thai speakers to verify it and suggest any necessary adjustments.
+The translated Winograd Schemas were reviewed with three native Thai speakers, and final adjustments were made to ensure clarity. A validator was provided with the translations and tasked with identifying any potential issues. They were instructed to pay closer attention to the text marked by the translators, which included changes to names and translations that the translators were unsure about. Based on the validator’s feedback, final adjustments to the translations were made, and typographical errors were corrected. Furthermore, the dataset will be made publicly available\footnote{A link to the dataset will be added in the de-anonymized version.}, inviting other native Thai speakers to verify it and suggest any necessary adjustments.
 
 # Dataset Structure
 
@@ -83,8 +83,19 @@ the passage the pronoun represents. An example instance looks like the following
 Only a test split is included.
 
 # Evaluation
-coming soon
+## Model Accuracy in English and Thai
 
+| Model           | Accuracy (English) | Accuracy (Thai) |
+|-----------------|--------------------|-----------------|
+| Typhoon         | 64.56%             | 54.04%          |
+| Claude-3-Haiku  | 62.81%             | 51.58%          |
+| Claude-3-Sonnet | 80.70%             | 63.51%          |
+| Claude-3-Opus   | 92.63%             | 77.54%          |
+| GPT-3.5         | 71.93%             | 51.23%          |
+| GPT-4           | 93.68%             | 72.28%          |
+| Human           | 90%                | -%              |
+
+**Table 1: Accuracy vs. Model in English and Thai**
 ## Acknowledgement 
 We extend our gratitude to Chanikarn Inthongpan and Korakoch Rienmek, who translated the schemas into the Thai language. We would also like to thank Sakrapee Namsak and Chonnawit Khumchoo for validating the translated Thai dataset.