2 research outputs found
Investigating Language Impact in Bilingual Approaches for Computational Language Documentation
For endangered languages, data collection campaigns have to accommodate the
challenge that many of them are from oral tradition, and producing
transcriptions is costly. Therefore, it is fundamental to translate them into a
widely spoken language to ensure interpretability of the recordings. In this
paper we investigate how the choice of translation language affects the
posterior documentation work and potential automatic approaches which will work
on top of the produced bilingual corpus. For answering this question, we use
the MaSS multilingual speech corpus (Boito et al., 2020) for creating 56
bilingual pairs that we apply to the task of low-resource unsupervised word
segmentation and alignment. Our results highlight that the choice of language
for translation influences the word segmentation performance, and that
different lexicons are learned by using different aligned translations. Lastly,
this paper proposes a hybrid approach for bilingual word segmentation,
combining boundary clues extracted from a non-parametric Bayesian model
(Goldwater et al., 2009a) with the attentional word segmentation neural model
from Godard et al. (2018). Our results suggest that incorporating these clues
into the neural models' input representation increases their translation and
alignment quality, specially for challenging language pairs.Comment: Accepted to 1st Joint SLTU and CCURL Worksho
Controlling Utterance Length in NMT-based Word Segmentation with Attention
International audienceOne of the basic tasks of computational language documentation (CLD) is to identifyword boundaries in an unsegmented phonemic stream. While several unsupervisedmonolingual word segmentation algorithms exist in the literature,they are challenged in real-world CLD settings by the small amount of availabledata. A possible remedy is to take advantage of glosses or translation in a foreign,well-resourced, language, which often exist for such data. In this paper, we explore and compareways to exploit neural machine translation models to perform unsupervised boundary detection with bilingual information, notably introducing a new loss function for jointly learning alignment and segmentation. We experiment with an actual under-resourced language, Mboshi, and show that these techniques can effectively control the output segmentation length