{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:07:44Z","timestamp":1767182864233},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Code-switching, the interleaving of two or more languages within a sentence or discourse is pervasive in multilingual societies. Accurate language models for code-switched text are critical for NLP tasks. State-of-the-art data-intensive neural language models are difficult to train well from scarce language-labeled code-switched text. A potential solution is to use deep generative models to synthesize large volumes of realistic code-switched text. Although generative adversarial networks and variational autoencoders can synthesize plausible monolingual text from continuous latent space, they cannot adequately address code-switched text, owing to their informal style and complex interplay between the constituent languages. We introduce VACS, a novel variational autoencoder architecture specifically tailored to code-switching phenomena. VACS encodes to and decodes from a two-level hierarchical representation, which models syntactic contextual signals in the lower level, and language switching signals in the upper layer. Sampling representations from the prior and decoding them produced well-formed, diverse code-switched sentences. Extensive experiments show that using synthetic code-switched text with natural monolingual data results in significant (33.06\\%) drop in perplexity.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/719","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"5175-5181","source":"Crossref","is-referenced-by-count":9,"title":["A Deep Generative Model for Code Switched Text"],"prefix":"10.24963","author":[{"given":"Bidisha","family":"Samanta","sequence":"first","affiliation":[{"name":"Indian Institute of Technology, Kharagpur"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sharmila","family":"Reddy","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology, Kharagpur"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hussain","family":"Jagirdar","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology, Kharagpur"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niloy","family":"Ganguly","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology, Kharagpur"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soumen","family":"Chakrabarti","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology, Bombay"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:51:20Z","timestamp":1564285880000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/719"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/719","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}