Jeremy For SMU School

README.md

@@ -10,7 +10,6 @@ language:
 # StickerQueries 🧷🗨️
 
 **StickerQueries** is a multilingual dataset for sticker query generation and retrieval. It features human-annotated query-sticker pairs that capture the expressive, emotional, and cultural semantics embedded in stickers.
-So far, the StickerQueries family has been downloaded over 200+ times!
 
 ## Dataset Structure
 
@@ -36,31 +35,20 @@ Each row in the CSV files includes:
 
 Explore the broader dataset: [U-Sticker](https://huggingface.co/datasets/metchee/u-sticker)
 
-## Update: V2
-We are working on an extended version and will update our labels into `_extended` folders/csv(s). Do stay tuned!
-
 ---
 
 ## Citation
 
-Our paper has been accepted at ACM Multimedia 2025!:-) If you find our work helpful, please do cite us with the following,
+If you use StickerQueries in your work, please cite us as:
 
 ```bibtex
-@inproceedings{10.1145/3746027.3758310,
-  author = {Chee, Heng Er Metilda and Wang, Jiayin and Guo, Zhiqiang and Ma, Weizhi and Zhang, Min},
-  title = {Small Stickers, Big Meanings: A Multilingual Sticker Semantic Understanding Dataset with a Gamified Approach},
-  year = {2025},
-  isbn = {9798400720352},
-  publisher = {Association for Computing Machinery},
-  address = {New York, NY, USA},
-  url = {https://doi.org/10.1145/3746027.3758310},
-  doi = {10.1145/3746027.3758310},
-  abstract = {Stickers, though small, are a highly condensed form of visual expression, ubiquitous across messaging platforms and embraced by diverse cultures, genders, and age groups. Despite their popularity, sticker retrieval remains an underexplored task due to the significant human effort and subjectivity involved in constructing high-quality query-sticker datasets. Although large language models (LLMs) excel at general NLP tasks, they falter when confronted with the nuanced, intangible, and highly specific nature of sticker query generation. To address the challenge of collecting diverse and contextually appropriate sticker search queries, we introduce Sticktionary, a gamified annotation framework designed to elicit high-quality, semantically rich queries from contributors. Using this framework, we construct StickerQueries, a multilingual dataset comprising 1,115 English and 615 Chinese sticker search queries, annotated by over 60 contributors across more than 60 hours of annotation. We demonstrate the utility of StickerQueries in several downstream tasks, including query generation and sticker retrieval. Through comprehensive quantitative and qualitative evaluations, we demonstrate that StickerQueries significantly improves the quality of query generation, retrieval accuracy, and semantic understanding within the sticker domain. To support future research, we publicly release the multilingual StickerQueries dataset and two fine-tuned query generation models. Additional experiments and supplementary case studies can be found here.},
-  booktitle = {Proceedings of the 33rd ACM International Conference on Multimedia},
-  pages = {13457–13463},
-  numpages = {7},
-  keywords = {heng er metilda chee, jiayin wang, min zhang, weizhi ma, zhiqiang guo},
-  location = {Dublin, Ireland},
-  series = {MM '25}
+@misc{chee2025smallstickersbigmeanings,
+      title={Small Stickers, Big Meanings: A Multilingual Sticker Semantic Understanding Dataset with a Gamified Approach}, 
+      author={Heng Er Metilda Chee and Jiayin Wang and Zhiqiang Guo and Weizhi Ma and Min Zhang},
+      year={2025},
+      eprint={2506.01668},
+      archivePrefix={arXiv},
+      primaryClass={cs.MM},
+      url={https://arxiv.org/abs/2506.01668}, 
 }
 ```

sticker_queries_extended_en_release.csv

@@ -1,48 +0,0 @@
-sticker_id,labeled_queries
-353547126392750436.gif,"sad, damn"
-359258651212383517.png, "cry, sad, tears"
-575666406691967042.gif, "swinging, happy, yay"
-575666406691967205.gif, "bew, love you!, love, kisses, xoxo, mua"
-672345197106954764.png, "sad, cry, wuwu, depressed, feeling down"
-908352511855824843.gif, "sad, damn"
-1043703381077524701.png, "are you sure, skeptical, unsure, uncertain"
-1043703381077524779.png, "um, roll eyes"
-1137162165791227928.gif, "haha, tears of joy, laughing, lol"
-1220028319907448892.gif, "joyful, haha, happy, yay, smiling"
-1688818743225877023.gif, "gross, puke, disgusting, stomach flip"
-1801939437249101857.gif, "swinging, happy, yay"
-1801939437249101878.gif, "blow fan, relax, chill"
-1801939437249101878.gif, "blow fan, relax, chill"
-2124661471689311590.png, "yawn, sleepy, boring"
-2193800188773013179.gif, "spinning, yay, sparkles, hooray, awesome"
-2193800188773013375.gif, "swinging, happy, yay"
-4031086588392898964.gif, "sad, damn"
-4110987462728221018.gif, "sad, wa, crying, upset, disappointed"
-4397782731392549014.png, "im ok, im fine, this is fine, sad, silently dying"
-5129967753624552159.png, "gross, puke, disgusting, stomach flip"
-5244555157504530660.png, "sad, stickman"
-5249206684395519221.png, "hehe, big eyes, curious"
-5328265783795521190.gif, "desperate, bawling, sad, pepe"
-5440777756398848195.gif, "cool, awesome, bling, shades, dope"
-5764614214593285315.png, "hehehe, anya, i see through you, steathy eyes, smirk"
-5767260335354417975.gif, "sad, damn"
-5769305212233715183.png, "internally screaming, internal dead"
-5787293347018184373.gif, "nom nom nom, eating"
-5807462066763598877.png, "sipping tea, drinking bubble tea, bbt, bubble tea time"
-5830199825492085559.gif, "sleepy, sleep, good night, yawn, tired"
-5886709753882611879.png, "hehehe, don't think i dont know, shify eyes, anya"
-5888702786211678440.gid, "hahaha, happy, smile, yay"
-5897689524242221957.png, "gross, puke, disgusting, stomach flip"
-5924548918271544949.png, "cries in a cool way, handsome cry"
-5942922019564686893.png, "gm, prawn, cool"
-5967491478765701666.gif, "blow fan, relax"
-5987554692639494010.png, "sad, upset, crying, baby"
-6021555470525794374.png, "yay, happy, >w<"
-6041832973202886166.gif, "swinging, happy, yay"
-6042137593758354487.gif, "swinging, happy, yay"
-6233005493715993516.gif, "sad, damn, dancing sad"
-6262641455253163706.png, "aaaaaa, why, !!"
-6307712442625425822.png, "hug, yay, happy"
-6073602464039307215.webm, "swinging fish, moyu"
-6129876903667962840.webm, "relax, lying down, so tired, dead"
-6041713581701992064.webm, "crying, sad, baby, upset"

stickers_extended/4997015572098057040.webm

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-size 154217

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