OasisSimp: An Open-source Asian-English Sentence Simplification Dataset

Hannah Liu; Muxin Tian; Iqra Ali; Haonan Gao; Qiaoyiwen Wu; Blair Yang; Uthayasanker Thayasivam; En-Shiun Annie Lee

cs.CLMar 14, 2026

OasisSimp: An Open-source Asian-English Sentence Simplification Dataset

Authors: Hannah Liu, Muxin Tian, Iqra Ali, Haonan Gao, Qiaoyiwen Wu, Blair Yang, Uthayasanker Thayasivam, En-Shiun Annie Lee,

Organizations: University of Toronto · Queen Mary University of London · Yale University · CoolWei AI Lab ·

Abstract

Sentence simplification aims to make complex text more accessible by reducing linguistic complexity while preserving the original meaning. However, progress in this area remains limited for mid-resource and low-resource languages due to the scarcity of high-quality data. To address this gap, we introduce the OasisSimp dataset, a multilingual dataset for sentence-level simplification covering five languages: English, Sinhala, Tamil, Pashto, and Thai. Among these, no prior sentence simplification datasets exist for Thai, Pashto, and Tamil, while limited data is available for Sinhala. Each language simplification dataset was created by trained annotators who followed detailed guidelines to simplify sentences while maintaining meaning, fluency, and grammatical correctness. We evaluate eight open-weight multilingual Large Language Models (LLMs) on the OasisSimp dataset and observe substantial performance disparities between high-resource and low-resource languages, highlighting the simplification challenges in multilingual settings. The OasisSimp dataset thus provides both a valuable multilingual resource and a challenging benchmark, revealing the limitations of current LLM-based simplification methods and paving the way for future research in low-resource sentence simplification. The dataset is available at https://OasisSimpDataset.github.io/.

Figures & tables

Lang	# Comp Sentences	Avg. Simp Sentences	Avg.Comp Length	Avg. Simp Length	Source Domain
English	2500	2.86	24.35	17.23	News
Sinhala	2500	5.00	30.12	28.78	Govt
Thai	1499	5.06	48.24	37.77	News
Tamil	520	4.66	23.22	17.65	Govt
Pashto	2500	3.00	28.81	20.31	Wiki

Table 1: Final statistics for each language in the OasisSimp dataset. Comp - complex, Simp - simplified.

Panel of Table 2 — Table 2: Sample Examples from the OasisSimp Dataset for each language.

Model	0 Shot					1 Shot					5 Shot
	SARI Comp.			SARI	F ref	SARI Comp.			SARI	F ref	SARI Comp.			SARI	F ref
	ADD	KEEP	DEL	SARI	F ref	ADD	KEEP	DEL	SARI	F ref	ADD	KEEP	DEL	SARI	F ref
Aya ✔	9.32	44.98	75.23	43.18	54.44	9.68	44.90	72.51	42.36	56.35	10.18	45.91	71.16	42.42	57.20
Cmd-R ✔	9.69	44.95	72.89	42.51	55.90	10.99	43.71	77.57	44.09	55.03	11.91	45.28	77.09	44.76	56.63
DeepSeek ✔	7.03	41.47	76.30	41.60	51.88	7.80	41.12	76.82	41.91	51.92	9.41	42.03	77.22	42.89	54.15
EuroLLM ✔	9.32	45.60	68.36	41.10	56.98	10.99	46.98	69.35	42.44	57.96	11.63	46.55	70.93	43.04	58.10

Table 3: Results on English ( OasisSimp-EN ) dataset : Zero, 1- and 5-shot Results. Highest value in bold and lowest underlined . ✔ : Language included in LLM training; ✔ : Likely included (Educated Guess); ✗ : Not included.

Model	0 Shot					1 Shot					5 Shot
	SARI Comp.			SARI	F ref	SARI Comp.			SARI	F ref	SARI Comp.			SARI	F ref
	ADD	KEEP	DEL	SARI	F ref	ADD	KEEP	DEL	SARI	F ref	ADD	KEEP	DEL	SARI	F ref
Aya ✗	0.47	23.11	68.49	30.69	54.94	0.40	26.01	67.39	31.27	54.22	0.21	27.62	65.75	31.20	44.78
Cmd-R ✗	0.18	29.95	65.05	31.73	58.32	0.19	30.24	65.08	31.84	55.94	0.13	28.87	65.18	31.40	45.34
DeepSeek ✗	0.16	16.92	68.71	28.60	-0.14	0.14	24.99	66.71	30.61	57.68	0.08	20.23	67.60	29.30	38.55
EuroLLM ✗	0.03	19.19	68.14	29.12	47.59	0.04	18.74	68.18	28.99	47.56	0.00	2.03	70.18	24.07	-60.47

Table 4: Results on Sinhala ( OasisSimp-SI ) dataset : Zero, 1- and 5-shot Results. Highest value in bold and lowest in underlined . ✔ : Language included in LLM training; ✔ : Likely included (Educated Guess); ✗ : Not included.

Model	0 Shot					1 Shot					5 Shot
	SARI Comp.			SARI	F ref	SARI Comp.			SARI	F ref	SARI Comp.			SARI	F ref
	ADD	KEEP	DEL	SARI	F ref	ADD	KEEP	DEL	SARI	F ref	ADD	KEEP	DEL	SARI	F ref
Aya ✗	0.33	21.01	84.30	35.22	56.49	0.38	24.45	83.53	36.12	59.01	0.43	25.98	82.59	36.33	60.66
Cmd-R ✗	0.47	29.02	82.19	37.23	62.17	0.51	26.88	81.85	36.41	61.84	0.45	28.89	81.86	37.07	62.26
DeepSeek ✗	0.12	15.27	84.34	33.24	27.98	0.18	15.80	83.95	33.31	51.68	0.14	15.77	84.09	33.34	40.95
EuroLLM ✗	0.15	26.35	80.43	35.64	52.25	0.23	26.26	80.23	35.57	60.26	0.16	28.05	77.71	35.31	62.57

Table 5: Results on Thai ( OasisSimp-TH ) dataset : Zero, 1- and 5-shot Results. Highest value in bold and lowest in underlined . ✔ : Language included in LLM training; ✔ : Likely included (Educated Guess); ✗ : Not included.

Model	0 Shot					1 Shot					5 Shot
	SARI Comp.			SARI	F ref	SARI Comp.			SARI	F ref	SARI Comp.			SARI	F ref
	ADD	KEEP	DEL	SARI	F ref	ADD	KEEP	DEL	SARI	F ref	ADD	KEEP	DEL	SARI	F ref
Aya ✗	2.42	18.48	70.60	30.50	72.42	2.25	27.49	68.73	32.82	74.40	2.00	34.27	66.62	34.30	76.24
Cmd-R ✗	2.78	26.40	69.28	32.82	74.14	2.07	30.78	66.61	33.15	74.97	1.44	34.40	66.15	34.00	76.08
DeepSeek ✗	0.33	11.88	70.83	27.68	-0.67	0.49	19.23	69.54	29.75	68.27	0.11	8.76	70.56	26.48	3.30
EuroLLM ✗	0.19	24.90	68.07	31.05	69.89	0.32	26.56	67.05	31.31	71.21	0.16	25.05	67.73	30.98	65.97

Table 6: Results on Tamil ( OasisSimp-TA ) dataset : Zero, 1- and 5-shot Results. Highest value in bold and lowest in underlined . ✔ : Language included in LLM training; ✔ : Likely included (Educated Guess); ✗ : Not included.

Model	0 Shot					1 Shot					5 Shot
	SARI Comp.			SARI	F ref	SARI Comp.			SARI	F ref	SARI Comp.			SARI	F ref
	ADD	KEEP	DEL	SARI	F ref	ADD	KEEP	DEL	SARI	F ref	ADD	KEEP	DEL	SARI	F ref
Aya ✗	0.62	23.98	67.47	30.69	49.17	1.08	45.60	58.62	35.10	60.83	1.77	53.81	47.17	34.25	68.25
Cmd-R ✗	0.75	50.82	51.73	34.44	61.91	0.93	54.41	44.44	33.26	67.84	0.70	56.53	35.62	30.95	70.52
DeepSeek ✗	0.52	41.19	60.71	34.14	38.65	0.90	48.83	54.59	34.78	63.65	0.91	50.16	52.51	34.53	66.26
EuroLLM ✗	0.50	54.28	44.40	33.06	67.55	0.65	54.87	43.28	32.93	69.72	0.78	55.37	42.09	32.75	70.42

Table 7: Results on Pashto ( OasisSimp-PS ) dataset : Zero, 1-, and 5-shot Results. Highest value in bold and lowest in underlined . ✔ : Language included in LLM training; ✔ : Likely included (Educated Guess); ✗ : Not included.

	English
Model	0 Shot	1 Shot	5 Shot
Aya	18.38	20.59	21.72
Cmd-R	21.03	19.58	20.72
DeepSeek	17.19	17.27	18.91
EuroLLM	23.07	23.07	22.79
Gemma	15.48	16.00	18.92

Table 8: BLEU scores on English ( OasisSimp-EN ) dataset across different shot settings. Highest value in bold and lowest underlined .

	Sinhala
Model	0 Shot	1 Shot	5 Shot
Aya	7.25	9.03	10.80
Cmd-R	11.86	11.90	11.63
DeepSeek	8.04	7.12	3.08
EuroLLM	2.24	2.19	0.00
Gemma	14.38	22.36	31.82

Table 9: BLEU scores on Sinhala( OasisSimp-SI ) dataset across different shot settings. Highest value in bold and lowest underlined .

	Thai
Model	0 Shot	1 Shot	5 Shot
Aya	7.70	9.65	11.58
Cmd-R	13.08	12.42	13.84
DeepSeek	2.24	5.99	5.71
EuroLLM	11.76	13.94	16.73
Gemma	3.67	11.20	14.43

Table 10: BLEU scores on Thai( OasisSimp-TH ) dataset across different shot settings. Highest value in bold and lowest underlined .

	Tamil
Model	0 Shot	1 Shot	5 Shot
Aya	11.26	20.69	29.97
Cmd-R	19.36	26.17	30.58
DeepSeek	3.67	10.11	1.48
EuroLLM	17.04	18.98	15.60
Gemma	9.62	21.36	36.71

Table 11: BLEU scores on Tamil( OasisSimp-TA ) dataset across different shot settings. Highest value in bold and lowest underlined .

	Pashto
Model	0 Shot	1 Shot	5 Shot
Aya	7.42	22.04	32.73
Cmd-R	28.39	33.99	38.49
DeepSeek	18.37	25.15	26.92
EuroLLM	33.74	34.65	35.54
Gemma	7.22	12.84	22.65

Table 12: BLEU scores on Pashto( OasisSimp-PS ) dataset across different shot settings. Highest value in bold and lowest underlined .

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Text simplification plays a crucial role in improving the accessibility and comprehensibility of written information for diverse audiences, including language learners and readers with limited literacy. Despite its importance, large-scale, high-quality datasets for training and evaluating text simplification models remain scarce for languages other than English. This paper reports an experimental study on the collection and processing of crowd-sourced simplification data from comparable corpora to construct a corpus suitable for both training and testing text simplification systems across multiple languages (Catalan, English, French, Italian and Spanish). We report mechanisms for sentence-level alignment from document-level data. The resulting dataset of the aligned sentence pairs is publicly available.

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