Special Interest Group on Computational Morphology and Phonology Workshop最新文献_第10页

SigMoreFun Submission to the SIGMORPHON Shared Task on Interlinear Glossing SigMoreFun提交给SIGMORPHON关于行间光泽的共享任务

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.sigmorphon-1.22

Taiqi He, Lindia Tjuatja, Nathaniel R. Robinson, Shinji Watanabe, David R. Mortensen, Graham Neubig, L. Levin

引用次数: 2

A future for universal grapheme-phoneme transduction modeling with neuralized finite-state transducers 用神经化有限状态换能器进行通用字素-音素转导建模的未来

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.sigmorphon-1.30

Chu-Cheng Lin

{"title":"A future for universal grapheme-phoneme transduction modeling with neuralized finite-state transducers","authors":"Chu-Cheng Lin","doi":"10.18653/v1/2023.sigmorphon-1.30","DOIUrl":"https://doi.org/10.18653/v1/2023.sigmorphon-1.30","url":null,"abstract":"We propose a universal grapheme-phoneme transduction model using neuralized finite-state transducers. Many computational models of grapheme-phoneme transduction nowadays are based on the (autoregressive) sequence-to-sequence string transduction paradigm. While such models have achieved state-of-the-art performance, they suffer from theoretical limitations of autoregressive models. On the other hand, neuralized finite-state transducers (NFSTs) have shown promising results on various string transduction tasks. NFSTs can be seen as a generalization of weighted finite-state transducers (WFSTs), and can be seen as pairs of a featurized finite-state machine (‘marked finite-state transducer’ or MFST in NFST terminology), and a string scoring function. Instead of taking a product of local contextual feature weights on FST arcs, NFSTs can employ arbitrary scoring functions to weight global contextual features of a string transduction, and therefore break the Markov property. Furthermore, NFSTs can be formally shown to be more expressive than (autoregressive) seq2seq models. Empirically, joint grapheme-phoneme transduction NFSTs have consistently outperformed vanilla seq2seq models on grapheme-tophoneme and phoneme-to-grapheme transduction tasks for English. Furthermore, they provide interpretable aligned string transductions, thanks to their finite-state machine component. In this talk, we propose a multilingual extension of the joint grapheme-phoneme NFST. We achieve this goal by modeling typological and phylogenetic features of languages and scripts as optional latent variables using a finite-state machine. The result is a versatile graphemephoneme transduction model: in addition to standard monolingual and multilingual transduction, the proposed multilingual NFST can also be used in various controlled generation scenarios, such as phoneme-to-grapheme transduction of an unseen language-script pair. We also plan to release an NFST software package.","PeriodicalId":186158,"journal":{"name":"Special Interest Group on Computational Morphology and Phonology Workshop","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115612345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Glossy Bytes: Neural Glossing using Subword Encoding 平滑字节:使用子字编码的神经光泽

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.sigmorphon-1.24

Ziggy Cross, Michelle Yun, Ananya Apparaju, Jata MacCabe, Garrett Nicolai, Miikka Silfverberg

引用次数: 1

Investigating Phoneme Similarity with Artificially Accented Speech 人工重音语音的音素相似性研究

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.sigmorphon-1.6

Margot Masson, Julie Carson-Berndsen

引用次数: 0

A Multilinear Approach to the Unsupervised Learning of Morphology 形态学无监督学习的多线性方法

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/W16-2020

A. Meyer, Markus Dickinson

引用次数: 0

Letter Sequence Labeling for Compound Splitting 化合物拆分的字母序列标记

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/W16-2012

Jianqiang Ma, Verena Henrich, E. Hinrichs

引用次数: 13

Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging 低资源黏着语形态标注的联合学习模型

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.sigmorphon-1.4

Gulinigeer Abudouwaili, Kahaerjiang Abiderexiti, Nian Yi, Aishan Wumaier

{"title":"Joint Learning Model for Low-Resource Agglutinative Language Morphological Tagging","authors":"Gulinigeer Abudouwaili, Kahaerjiang Abiderexiti, Nian Yi, Aishan Wumaier","doi":"10.18653/v1/2023.sigmorphon-1.4","DOIUrl":"https://doi.org/10.18653/v1/2023.sigmorphon-1.4","url":null,"abstract":"Due to the lack of data resources, rule-based or transfer learning is mainly used in the morphological tagging of low-resource languages. However, these methods require expert knowledge, ignore contextual features, and have error propagation. Therefore, we propose a joint morphological tagger for low-resource agglutinative languages to alleviate the above challenges. First, we represent the contextual input with multi-dimensional features of agglutinative words. Second, joint training reduces the direct impact of part-of-speech errors on morphological features and increases the indirect influence between the two types of labels through a fusion mechanism. Finally, our model separately predicts part-of-speech and morphological features. Part-of-speech tagging is regarded as sequence tagging. When predicting morphological features, two-label adjacency graphs are dynamically reconstructed by integrating multilingual global features and monolingual local features. Then, a graph convolution network is used to learn the higher-order intersection of labels. A series of experiments show that the proposed model in this paper is superior to other comparative models.","PeriodicalId":186158,"journal":{"name":"Special Interest Group on Computational Morphology and Phonology Workshop","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116037226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generalized Glossing Guidelines: An Explicit, Human- and Machine-Readable, Item-and-Process Convention for Morphological Annotation 广义擦光指南:一种明确的、人类和机器可读的、用于形态注释的项目和过程约定

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.sigmorphon-1.7

David R. Mortensen, Ela Gulsen, Taiqi He, Nathaniel R. Robinson, Jonathan D. Amith, Lindia Tjuatja, L. Levin

引用次数: 0

Translating a low-resource language using GPT-3 and a human-readable dictionary 使用GPT-3和人类可读的字典翻译低资源语言

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.sigmorphon-1.2

M. Elsner, Jordan Needle

引用次数: 0

Lightweight morpheme labeling in context: Using structured linguistic representations to support linguistic analysis for the language documentation context 上下文中的轻量级语素标记:使用结构化语言表示来支持语言文档上下文的语言分析

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.sigmorphon-1.9

Bhargav Shandilya, Alexis Palmer

{"title":"Lightweight morpheme labeling in context: Using structured linguistic representations to support linguistic analysis for the language documentation context","authors":"Bhargav Shandilya, Alexis Palmer","doi":"10.18653/v1/2023.sigmorphon-1.9","DOIUrl":"https://doi.org/10.18653/v1/2023.sigmorphon-1.9","url":null,"abstract":"Linguistic analysis is a core task in the process of documenting, analyzing, and describing endangered and less-studied languages. In addition to providing insight into the properties of the language being studied, having tools to automatically label words in a language for grammatical category and morphological features can support a range of applications useful for language pedagogy and revitalization. At the same time, most modern NLP methods for these tasks require both large amounts of data in the language and compute costs well beyond the capacity of most research groups and language communities. In this paper, we present a gloss-to-gloss (g2g) model for linguistic analysis (specifically, morphological analysis and part-of-speech tagging) that is lightweight in terms of both data requirements and computational expense. The model is designed for the interlinear glossed text (IGT) format, in which we expect the source text of a sentence in a low-resource language, a translation of that sentence into a language of wider communication, and a detailed glossing of the morphological properties of each word in the sentence. We first produce silver standard parallel glossed data by automatically labeling the high-resource translation. The model then learns to transform source language morphological labels into output labels for the target language, mediated by a structured linguistic representation layer. We test the model on both low-resource and high-resource languages, and find that our simple CNN-based model achieves comparable performance to a state-of-the-art transformer-based model, at a fraction of the computational cost.","PeriodicalId":186158,"journal":{"name":"Special Interest Group on Computational Morphology and Phonology Workshop","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134260124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0