La production d’annotations linguistiques ou gloses interlinéaires explicitant le sens ou la fonction de chaque unité repérée dans un enregistrement source (ou dans sa transcription) est une étape importante du processus de documentation des langues. Ces gloses exigent une très grande expertise de la langue documentée et un travail d’annotation fastidieux. Notre étude s’intéresse à l’automatisation partielle de ce processus. Il s’appuie sur la partition des gloses en deux types : les gloses grammaticales exprimant une fonction grammaticale, les gloses lexicales indiquant les unités de sens. Notre approche repose sur l’hypothèse d’un alignement entre les gloses lexicales et une traduction ainsi que l’utilisation de Lost, un modèle probabiliste de traduction automatique. Nos expériences sur une langue en cours de documentation, le tsez, montrent que cet apprentissage est effectif même avec un faible nombre de phrases de supervision.

This paper describes LISN”’“s submission to the second track (open track) of the shared task on Interlinear Glossing for SIGMORPHON 2023. Our systems are based on Lost, a variation of linear Conditional Random Fields initially developed as a probabilistic translation model and then adapted to the glossing task. This model allows us to handle one of the main challenges posed by glossing, i.e. the fact that the list of potential labels for lexical morphemes is not fixed in advance and needs to be extended dynamically when labelling units are not seen in training. In such situations, we show how to make use of candidate lexical glosses found in the translation and discuss how such extension affects the training and inference procedures. The resulting automatic glossing systems prove to yield very competitive results, especially in low-resource settings.

Language documentation often requires segmenting transcriptions of utterances collected on the field into words and morphemes. While these two tasks are typically performed in succession, we study here Bayesian models for simultaneously segmenting utterances at these two levels. Our aim is twofold: (a) to study the effect of explicitly introducing a hierarchy of units in joint segmentation models; (b) to further assess whether these two levels can be better identified through weak supervision. For this, we first consider a deterministic coupling between independent models; then design and evaluate hierarchical Bayesian models. Experiments with two under-resourced languages (Japhug and Tsez) allow us to better understand the value of various types of weak supervision. In our analysis, we use these results to revisit the distributional hypotheses behind Bayesian segmentation models and evaluate their validity for language documentation data.

Interlinear Morphological Glosses are annotations produced in the context of language documentation. Their goal is to identify morphs occurring in an L1 sentence and to explicit their function and meaning, with the further support of an associated translation in L2. We study here the task of automatic glossing, aiming to provide linguists with adequate tools to facilitate this process. Our formalisation of glossing uses a latent variable Conditional Random Field (CRF), which labels the L1 morphs while simultaneously aligning them to L2 words. In experiments with several under-resourced languages, we show that this approach is both effective and data-efficient and mitigates the problem of annotating unknown morphs. We also discuss various design choices regarding the alignment process and the selection of features. We finally demonstrate that it can benefit from multilingual (pre-)training, achieving results which outperform very strong baselines.

La segmentation automatique en mots et en morphèmes est une étape cruciale dans le processus de documentation des langues. Dans ce travail, nous étudions plusieurs modèles bayésiens pour réaliser une segmentation conjointe des phrases à ces deux niveaux : d’une part, en introduisant un couplage déterministe entre deux modèles spécialisés pour identifier chaque type de frontières, d’autre part, en proposant une modélisation intrinsèquement hiérarchique. Un objectif important de cette étude est de comparer ces modèles dans un scénario où une supervision faible est disponible. Nos expériences portent sur deux langues et permettent de comparer dans des conditions réalistes les mérites de ces diverses modélisations.

Word and morpheme segmentation are fundamental steps of language documentation as they allow to discover lexical units in a language for which the lexicon is unknown. However, in most language documentation scenarios, linguists do not start from a blank page: they may already have a pre-existing dictionary or have initiated manual segmentation of a small part of their data. This paper studies how such a weak supervision can be taken advantage of in Bayesian non-parametric models of segmentation. Our experiments on two very low resource languages (Mboshi and Japhug), whose documentation is still in progress, show that weak supervision can be beneficial to the segmentation quality. In addition, we investigate an incremental learning scenario where manual segmentations are provided in a sequential manner. This work opens the way for interactive annotation tools for documentary linguists.

We propose approaches to Quality Estimation (QE) for Machine Translation that explore both text and visual modalities for Multimodal QE. We compare various multimodality integration and fusion strategies. For both sentence-level and document-level predictions, we show that state-of-the-art neural and feature-based QE frameworks obtain better results when using the additional modality.