Metraipolis › archive › dc_ecbd73aebe6f

S2TDR: Structured Multimodal Reconstruction for Sticker-Aware Dialogue Understanding — arXiv:2610.41176

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41176
License (as archived): CC0-1.0
Archived: 2026-11-03T07:45:19.341Z
SHA-256 of the archived text: 8c41a32d6f4cd008d371a4c1a70eb639a0a8bdb33d3f1ec7f6df5c64c5bbc092
Stable URL of this page: https://metraipolis.com/documento/dc_ecbd73aebe6f

How to cite

S2TDR: Structured Multimodal Reconstruction for Sticker-Aware Dialogue Understanding — arXiv:2610.41176 — arXiv (en), https://arxiv.org/abs/2610.41176 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ecbd73aebe6f (Metraipolis archive dc_ecbd73aebe6f, sha256 8c41a32d6f4cd008d371a4c1a70eb639a0a8bdb33d3f1ec7f6df5c64c5bbc092)

BibTeX

@misc{dcecbd73aebe6f,
  title = {S2TDR: Structured Multimodal Reconstruction for Sticker-Aware Dialogue Understanding — arXiv:2610.41176},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41176},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ecbd73aebe6f; sha256 8c41a32d6f4cd008d371a4c1a70eb639a0a8bdb33d3f1ec7f6df5c64c5bbc092},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41176. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Stickers are widely used in social media dialogues to express affect, stance, and intent beyond literal text. Their meanings are often non-literal and context-dependent, making them difficult to handle with generic visual fusion or standalone captioning. We propose Sticker-to-Text Dialogue Reconstruction (S2TDR), a structured multimodal reconstruction framework that converts sticker turns into contextually appropriate textual utterances. S2TDR decomposes sticker interpretation into visual cue extraction, affective-intent inference, and context-aware dialogue reconstruction, using OCR text only as an auxiliary cue when available. The reconstructed dialogue can then be consumed by existing text-based dialogue models. Experiments on sentiment analysis, intent recognition, and dialogue summarization show that S2TDR generally improves multiple downstream models over direct sticker integration methods. Further analyses on component ablations, OCR-empty stickers, and reconstruction quality indicate that structured reconstruction provides an interpretable and effective way to integrate sticker semantics into dialogue understanding.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.41176 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T07:39:52.165Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).