LISN @ SIGMORPHON 2023 Shared Task on Interlinear Glossing (2023)

Abstract: 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.

## introduction lisn participated in the 'open track' of the shared task on interlinear glossing of sigmorphon 2023 @xcite with two submissions. figure 1 presents the format of the sentences for this shared task. in this track, the source sentence t is overtly segmented into morphemes (m), which yields an explicit one-to-one correspondence between each source morpheme and the corresponding gloss (g), thanks to the leipzig glossing rules convention @xcite . a translation l in a more-resourced language (english or spanish) is also provided, except for nyangbo. an obvious formalisation of the task that we mostly adopt, is thus to view glossing as a sequence labelling task performed at the morpheme level. as can be seen in figure 1 , there are roughly two categories of glosses: grammatical glosses indicating the grammatical function of the morpheme (e.g., gen1) and lexical glosses expressing a meaning (e.g., son). 1 while the grammatical glosses to accommodate such cases, we assume that lexical glosses can be directly inferred from the translation tier. recent works on automatic gloss generation, such as @xcite , also rely on a similar assumption and leverage the available translations. in our model, we will thus consider that the set of possible labels for the morphemes in any given sentence consists of the union of (a) all the grammatical glosses, (b) lemmas occurring in the target translation, (c) frequently-associated labels from the training data. by using a variant of conditional random fields (crfs) @xcite , which enables such local restriction of the set of possible labels, our glossing model can be viewed as an extension of previous sequence labelling systems based on crfs such as @xcite @xcite . in our approach, using translations as labels during training raises the issue of aligning the translation and the source sentence, which we handle with the neural word alignment model of jalili @xcite . as alignments are computed at the morpheme level, this technique does not apply for the 'closed track', where the source segmentation is not part of the training annotations. our participation is motivated by two factors: lexical glosses in our submission. to evaluate the model performance across varying training data sizes (from a few dozen to thousands of sentences) and to challenge its ability to handle a variety of high-resource languages in the target translation. section 2 describes our system, while section 3 presents our experimental settings. section 4 reports the complete set of results obtained with our models. g {lat gen1 ... iii pst.unw} m nesi s ł q ono uži zow n t he had three sons d (or r) ... he.obl be.nprs ... 2 system description our glossing system uses two main technological components: we (a) rely on an automatic alignment model between the lexical glosses and the target translation during training, which also allows us to exploit additional information regarding target words, such as their part-of-speech (pos) tag or their position; (b) use an extended version of crfs which allows us to locally restrict the set of possible labels to carry out the glossing task. figure 2 summarises the main ideas behind our approach. ## experimental conditions we have run s2 on tsez and uspanteko subsequently and will also report these results below. ## results compared to other submitted systems, we reached the best word accuracy for gitksan and natugu and the best morpheme accuracy for natugu. ## conclusion assuming the lexical glosses can be aligned with words in the target translation, we repurposed a statistical machine translation system based on a globally-normalised model, akin to crfs, that allows us to dynamically define a local set of labels for the automatic gloss generation task. using two sets of features, our systems are compatible in lowand very low-resource settings and outperformed the baseline models according to several evaluation metrics. we plan on further exploring feature functions on both source and target sides. besides, since our systems rely on automatic alignments, which may contain and project some noise, we will try to remove this dependency modelling alignment as an unobserved variable in a latent variable model. furthermore, as our submission focused on lowresource data conditions, we did not consider neural methods, which are notably data-intensive; future work would be to integrate word embeddings for better-resourced languages such as arapaho. our code is available at: https://github.com/ shuokabe/gloss_lost .