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Users of Augmentative and Alternative Communication (AAC) may write letter-by-letter via an interface that uses a character language model. However, most state-of-the-art large pretrained language models predict subword tokens of variable length. We investigate how to practically use such models to make accurate and efficient character predictions. Our algorithm for producing character predictions from a subword large language model (LLM) provides more accurate predictions than using a classification layer, a byte-level LLM, or an n-gram model. Additionally, we investigate a domain adaptation procedure based on a large dataset of sentences we curated based on scoring how useful each sentence might be for spoken or written AAC communication. We find our procedure further improves model performance on simple, conversational text.
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Byte-based Multilingual {NMT} for Endangered Languages
"Multilingual neural machine translation (MNMT) jointly trains a shared model for translation with multiple language pairs. However, traditional subword-based MNMT approaches suffer from out-of-vocabulary (OOV) issues and representation bottleneck, which often degrades translation performance on certain language pairs. While byte tokenization is used to tackle the OOV problems in neural machine translation (NMT), until now its capability has not been validated in MNMT. Additionally, existing work has not studied how byte encoding can benefit endangered language translation to our knowledge. We propose a byte-based multilingual neural machine translation system (BMNMT) to alleviate the representation bottleneck and improve translation performance in endangered languages. Furthermore, we design a random byte mapping method with an ensemble prediction to enhance our model robustness. Experimental results show that our BMNMT consistently and significantly outperforms subword/word-based baselines on twelve language pairs up to +18.5 BLEU points, an 840{\%} relative improvement.",
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- Award ID(s):
- 2113906
- PAR ID:
- 10514658
- Editor(s):
- Calzolari, Nicoletta; Huang, Chu-Ren; Kim, Hansaem; Pustejovsky, James; Wanner, Leo; Choi, Key-Sun; Ryu, Pum-Mo; Chen, Hsin-Hsi; Donatelli, Lucia; Ji, Heng; Kurohashi, Sadao; Paggio, Patrizia; Xue, Nianwen; Kim, Seokhwan; Hahm, Younggyun; He, Zhong; Lee, Tony Kyungil; Santus, Enrico; Bond, Francis; Na, Seung-Hoon
- Publisher / Repository:
- International Committee on Computational Linguistics
- Date Published:
- Format(s):
- Medium: X
- Location:
- Gyeongju, Republic of Korea
- Sponsoring Org:
- National Science Foundation
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