NoDaLiDa 2023 - May 22-24, 2023


Slaapte or Sliep? Extending Neural-Network Simulations of English Past Tense Learning to Dutch and German

Xiulin Yang, Jingyan Chen, Arjan van Eerden, Ahnaf Mozib Samin, Arianna Bisazza

This work studies the plausibility of sequence-to-sequence neural networks as models of morphological acquisition by humans. We replicate the findings of Kirov and Cotterell (2018) on the well-known challenge of the  English past tense and examine their generalizability to two related but morphologically richer languages, namely Dutch and German. Using a new dataset of English/Dutch/German (ir)regular verb forms, we show that the major findings of Kirov and Cotterell (2018) hold for all three languages, including the observation of over-regularization errors and micro U-shape learning trajectories. At the same time, we observe troublesome cases of non human-like errors similar to those reported by recent follow-up studies with different languages or neural architectures. Finally, we study the possibility of switching to orthographic input in the absence of pronunciation information and show this can have a non-negligible impact on the simulation results, with possibly misleading findings. 

Length Dependence of Vocabulary Richness

Niklas Zechner

The relation between the length of a text and the number of unique words is investigated using several Swedish language corpora. We consider a number of existing measures of vocabulary richness, show that they are not length-independent, and try to improve on some of them based on statistical evidence. We also look at the spectrum of values over text lengths, and find that genres have characteristic shapes. 

You say tomato, I say the same: A large-scale study of linguistic accommodation in online communities

Aleksandrs Berdicevskis, Viktor Erbro

An important assumption in sociolinguistics and cognitive psychology is that human beings adjust their language use to their interlocutors. Put simply, the more often people talk (or write) to each other, the more similar their speech becomes. Such accommodation has often been observed in small-scale observational studies and experiments, but large-scale longitudinal studies that systematically test whether the accommodation occurs are scarce. We use data from a very large Swedish online discussion forum to show that linguistic production of the users who write in the same subforum does usually become more similar over time. Moreover, the results suggest that this trend tends to be stronger for those pairs of users who actively interact than for those pairs who do not interact. Our data thus support the accommodation hypothesis. 

Identifying Token-Level Dialectal Features in Social Media

Jeremy Barnes, Samia Touileb, Petter Mæhlum, Pierre Lison

Dialectal variation is present in many human languages and is attracting a growing interest in NLP. Most previous work concentrated on either (1) classifying dialectal varieties at the document or sentence level or (2) performing standard NLP tasks on dialectal data. In this paper, we propose the novel task of token-level dialectal feature prediction. We present a set of fine-grained annotation guidelines for Norwegian dialects, expand a corpus of dialectal tweets, and manually annotate them using the introduced guidelines. Furthermore, to evaluate the learnability of our task, we conduct labeling experiments using a collection of baselines, weakly supervised and supervised sequence labeling models. The obtained results show that, despite the difficulty of the task and the scarcity of training data, many dialectal features can be predicted with reasonably high accuracy.