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Hence, several works have looked into automatically inferring it from data, e.g., actual conversations between a customer and a support agent. To recover such dialogue structure, recent methods based on discrete variational models learn to jointly encode and cluster utterances in dialogue states, but (i) represent utterances by only considering preceding dialogue context, and (ii) are slow to train since they are optimized with a compute-expensive <jats:italic>decoding<\/jats:italic> objective. We revisit and improve upon an existing efficient pipeline approach, commonly adopted as a baseline, that first encodes utterances and then clusters them with <jats:italic>k<\/jats:italic>-means to induce the dialogue structure. However, the existing approach represents utterances as bag-of-words or skip-thought vectors, which have been shown to perform poorly in semantic similarity tasks, and without considering dialogue context. We therefore first investigate the use of more powerful transformer-based encoders for encoding utterances. Next, we propose <jats:sc>ell<\/jats:sc>o<jats:sc>dar<\/jats:sc>, a method for learning representations that capture both preceding and subsequent dialogue context, inspired by word-to-vec training strategies. <jats:sc>ell<\/jats:sc>o<jats:sc>dar<\/jats:sc> is efficient since representations are learned directly in the encoding space by finetuning just a <jats:italic>single<\/jats:italic> linear layer on top of a <jats:italic>frozen<\/jats:italic> sentence encoder with a <jats:italic>vector-to-vector<\/jats:italic> regression training objective. Extensive experiments on representative datasets for dialogue structure induction (SimDial, Schema Guided Dialogues, DSTC2, and CamRest676) demonstrate that in terms of effectiveness to induce the correct dialogue structure, (i) clustering utterances represented by transformed-based encoders improves recent joint models by 13%\u201332% on standard cluster metrics, and (ii) clustering <jats:sc>ell<\/jats:sc>o<jats:sc>dar<\/jats:sc>\u2019s representations yields additional improvements ranging from +20% to +26%, with speedups of <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\times $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u00d7<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>\u00a0<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\textbf{10}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mn>10<\/mml:mn>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>\u2013<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\textbf{10}^{\\textbf{4}}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mn>10<\/mml:mn>\n                    <mml:mn>4<\/mml:mn>\n                  <\/mml:msup>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> compared to the recent joint models.<\/jats:p>","DOI":"10.1007\/s10489-024-05455-5","type":"journal-article","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T05:01:56Z","timestamp":1713416516000},"page":"5278-5305","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Revisiting clustering for efficient unsupervised dialogue structure induction"],"prefix":"10.1007","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9661-1206","authenticated-orcid":false,"given":"Maarten De","family":"Raedt","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fr\u00e9deric","family":"Godin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chris","family":"Develder","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Demeester","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,18]]},"reference":[{"key":"5455_CR1","doi-asserted-by":"publisher","unstructured":"Casanueva I, Tem\u010dinas T, Gerz D, Henderson M, Vuli\u0107 I (2020) Efficient intent detection with dual sentence encoders. 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The SimDial dataset [] is licensed under the Apache License 2.0, which permits modification and distribution with a copyright notice. The SGD dataset [] is licensed under CC-BY-4.0, which allows modification and redistribution under the same license and with a copyright notice. CamRest676 [, ] has been widely adopted in previous studies and has no specific license. The DSTC2 [] dataset is licensed under the GPL-3.0 License, which permits modification and redistribution under the conditions of the license and copyright notice, as well as stating changes and disclosing the source code.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}]}}