{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T18:12:50Z","timestamp":1696097570118},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684369","type":"print"},{"value":"9781643684376","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,28]]},"abstract":"<jats:p>The Adapter framework introduces lightweight modules that reduce the complexity of Multi-Domain Machine Translation systems. Compared to fine-tuned models, Adapters train faster, do not overfit, have smaller memory requirements, and maintain the base model intact. However, just like fine-tuned models, they need prior information about the domain of the sentence. Otherwise, their performance decreases for out-of-domain and unknown-domain samples. In this work, we propose a solution that does not require the\u00a0information and can decide on the sample\u2019s origin on-the-fly without compromising quality or latency. We introduce a built-in gating mechanism utilising a knowledge distillation framework to activate a subset of softly-gated, domain-specific Adapters that are relevant to the sentence. The effectiveness of the proposed solution is demonstrated through our experiments on two language pairs, using both in-domain and out-of-domain datasets. Our analysis reveals that Gated Adapters provide significant benefits, particularly in the case of ambiguous, misclassified samples, resulting in an improvement of over\u00a0+5\u00a0COMET points.<\/jats:p>","DOI":"10.3233\/faia230404","type":"book-chapter","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:11:42Z","timestamp":1695978702000},"source":"Crossref","is-referenced-by-count":0,"title":["Gated Adapters for Multi-Domain Neural Machine Translation"],"prefix":"10.3233","author":[{"given":"Mateusz","family":"Klimaszewski","sequence":"first","affiliation":[{"name":"Warsaw University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeno","family":"Belligoli","sequence":"additional","affiliation":[{"name":"Booking.com"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Satendra","family":"Kumar","sequence":"additional","affiliation":[{"name":"Booking.com"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emmanouil","family":"Stergiadis","sequence":"additional","affiliation":[{"name":"Booking.com"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2023"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230404","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:11:43Z","timestamp":1695978703000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230404"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"ISBN":["9781643684369","9781643684376"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230404","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,28]]}}}