@inproceedings{oh-etal-2026-template,
title = "Template-assisted Contrastive Learning of Task-oriented Dialogue Sentence Embeddings",
author = "Oh, Minsik and
Li, Jiwei and
Wang, Guoyin",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1015/",
pages = "22181--22198",
ISBN = "979-8-89176-390-6",
abstract = "Learning high quality sentence embeddings from dialogues has drawn increasing attentions as it is essential to solve a variety of dialogue-oriented tasks with low annotation cost. Annotating and gathering utterance relationships in conversations are difficult, while token-level annotations, , entities, slots and templates, are much easier to obtain. Other sentence embedding methods are usually sentence-level self-supervised frameworks and cannot utilize token-level extra knowledge. We introduce Template-aware Dialogue Sentence Embedding (TaDSE), a novel augmentation method that utilizes template information to learn utterance embeddings via self-supervised contrastive learning framework. We further enhance the effect with a synthetically augmented dataset that diversifies utterance-template association, in which slot-filling is a preliminary step. We evaluate TaDSE performance on five downstream benchmark dialogue datasets. The experiment results show that TaDSE achieves significant improvements over previous SOTA methods for dialogue. We further introduce a novel analytic instrument of semantic compression test, for which we discover a correlation with uniformity and alignment. Our code is available at https://github.com/minsik-ai/Template-Contrastive-Embedding"
}Markdown (Informal)
[Template-assisted Contrastive Learning of Task-oriented Dialogue Sentence Embeddings](https://preview.aclanthology.org/ingest-acl/2026.acl-long.1015/) (Oh et al., ACL 2026)
ACL