We propose a sequence labelling approach to word-level morpheme segmentation. Segmentation labels are edit operations derived from a modified minimum edit distance alignment. We show that sequence labelling performs well for “shallow segmentation” and “canonical segmentation”, achieving 96.06 f1 score (macroaveraged over all languages in the shared task) and ranking 3rd among all participating teams. Therefore, we conclude that sequence labelling is a promising approach to morpheme segmentation.

pdf/sigmorphon-2022-shared-task-on-morpheme-segmentation-38znhe3i.pdf

SIGMORPHON 2022 Shared Task on Morpheme Segmentation Submission Description: Sequence Labelling for Word-Level Morpheme Segmentation

This paper presents the submission of team NUM DI to the SIGMORPHON 2022 Task on Morpheme Segmentation Part 1, word-level morpheme segmentation. We explore the transformer neural network approach to the shared task. We develop monolingual models for world-level morpheme segmentation and focus on improving the model by using various training strategies to improve accuracy and generalization across languages.

/pdf/word-level-morpheme-segmentation-using-transformer-neural-1be4s94g.pdf

Word-level Morpheme segmentation using Transformer neural network

The popular subword tokenizers of current language models, such as Byte-Pair Encoding (BPE), are known not to respect morpheme boundaries, which affects the downstream performance of the models. While many improved tokenization algorithms have been proposed, their evaluation and cross-comparison is still an open problem. As a solution, we propose a combined intrinsic-extrinsic evaluation framework for subword tokenization. Intrinsic evaluation is based on our new UniMorph Labeller tool that classifies subword tokenization as either morphological or alien. Extrinsic evaluation, in turn, is performed via the Out-of-Vocabulary Generalization Challenge 1.0 benchmark, which consists of three newly specified downstream text classification tasks. Our empirical findings show that the accuracy of UniMorph Labeller is 98%, and that, in all language models studied (including ALBERT, BERT, RoBERTa, and DeBERTa), alien tokenization leads to poorer generalizations compared to morphological tokenization for semantic compositionality of word meanings.

Evaluating Subword Tokenization: Alien Subword Composition and OOV Generalization Challenge

This paper describes the JB132 submission to the SIGMORPHON 2022 Shared Task 3 on Morpheme Segmentation. In this paper we describe probabilistic model trained with the Expectation-Maximization algorithm, we provide the results and analyze sources of errors and general limitations of our approach. The model was implemented within our own modular probabilistic framework.

JB132 submission to the SIGMORPHON 2022 Shared Task 3 on Morphological Segmentation

While many languages possess processes of joining two or more words to create compound words, previous studies have been typically limited only to languages with excessively productive compound formation (e.g., German, Dutch) and there is no public dataset containing compound and non-compound words across a large number of languages. In this work, we systematically study decompounding, the task of splitting compound words into their constituents, at a wide scale. We first address the data gap by introducing a dataset of 255k compound and non-compound words across 56 diverse languages obtained from Wiktionary. We then use this dataset to evaluate an array of Large Language Models (LLMs) on the decompounding task. We find that LLMs perform poorly, especially on words which are tokenized unfavorably by subword tokenization. We thus introduce a novel methodology to train dedicated models for decompounding. The proposed two-stage procedure relies on a fully self-supervised objective in the first stage, while the second, supervised learning stage optionally fine-tunes the model on the annotated Wiktionary data. Our self-supervised models outperform the prior best unsupervised decompounding models by 13.9% accuracy on average. Our fine-tuned models outperform all prior (language-specific) decompounding tools. Furthermore, we use our models to leverage decompounding during the creation of a subword tokenizer, which we refer to as CompoundPiece. CompoundPiece tokenizes compound words more favorably on average, leading to improved performance on decompounding over an otherwise equivalent model using SentencePiece tokenization.

pdf/compoundpiece-evaluating-and-improving-decompounding-qa1pqot2.pdf

CompoundPiece: Evaluating and Improving Decompounding Performance of Language Models

The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections. Subtask 1, word-level morpheme segmentation, covered 5 million words in 9 languages (Czech, English, Spanish, Hungarian, French, Italian, Russian, Latin, Mongolian) and received 13 system submissions from 7 teams and the best system averaged 97.29% F1 score across all languages, ranging English (93.84%) to Latin (99.38%). Subtask 2, sentence-level morpheme segmentation, covered 18,735 sentences in 3 languages (Czech, English, Mongolian), received 10 system submissions from 3 teams, and the best systems outperformed all three state-of-the-art subword tokenization methods (BPE, ULM, Morfessor2) by 30.71% absolute. To facilitate error analysis and support any type of future studies, we released all system predictions, the evaluation script, and all gold standard datasets.

The SIGMORPHON 2022 Shared Task on Morpheme Segmentation

Dystonia in children with acquired brain injury.

Natural language processing is largely focused on written text processing. However, many computational linguists tacitly endorse myths about the nature of writing. We highlight two of these myths—the conflation of language and writing, and the notion that Chinese, Japanese, and Korean writing is ideographic—and suggest how the community can dispel them.

Myths about Writing Systems in Speech & Language Technology

Abstract Autism spectrum disorder (ASD) is a common neurodevelopmental condition. The American Academy of Paediatrics and American Academy of Neurology do not recommend routine brain magnetic resonance imaging (MRI) in the assessment of ASD. The need for a brain MRI should be decided on atypical features in the clinical history and examination. However, many physicians continue to use brain MRI routinely in the assessment process. We performed a retrospective review of indications for requesting brain MRI in our institution over a 5-year period. The aim was to identify the yield of MRI in children with ASD and calculate the prevalence of significant neuroimaging abnormalities in children with ASD and identify clinical indications for neuroimaging. One hundred eighty-one participants were analysed. An abnormal brain MRI was identified in 7.2% (13/181). Abnormal brain MRI was more likely with an abnormal neurological examination (OR 33.1, p = 0.001) or genetic/metabolic abnormality (OR 20, p = 0.02). In contrast, abnormal MRI was not shown to be more likely in children with a variety of other indications such as behavioural issues and developmental delay. Conclusion : Thus, our findings support that MRI should not be a routine investigation in ASD, without additional findings. The decision to arrange brain MRI should be made on a case-by-case basis following careful evaluation of potential risks and benefits. The impact of any findings on the management course of the child should be considered prior to arranging imaging. What is Known: • Incidental brain MRI findings are common in children with and without ASD. • Many children with ASD undergo brain MRI in the absence of neurological comorbidities. What is New: • Brain MRI abnormalities in ASD are more likely with an abnormal neurological examination and genetic or metabolic conditions. • Prevalence of significant brain MRI abnormalities in ASD alone is low. 

/pdf/yield-of-brain-mri-in-children-with-autism-spectrum-disorder-2i214pr3.pdf

Yield of brain MRI in children with autism spectrum disorder.

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The SIGMORPHON 2022 Shared Task on Morpheme Segmentation

Dystonia in children with acquired brain injury.

Myths about Writing Systems in Speech & Language Technology

Yield of brain MRI in children with autism spectrum disorder.