惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

B
Blog
Hugging Face - Blog
Hugging Face - Blog
月光博客
月光博客
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
人人都是产品经理
人人都是产品经理
博客园 - Franky
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
P
Proofpoint News Feed
F
Fortinet All Blogs
H
Help Net Security
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
V
Visual Studio Blog
Jina AI
Jina AI
J
Java Code Geeks
Blog — PlanetScale
Blog — PlanetScale
S
SegmentFault 最新的问题
D
DataBreaches.Net
T
The Blog of Author Tim Ferriss
美团技术团队
博客园 - 司徒正美
宝玉的分享
宝玉的分享
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Apple Machine Learning Research
Apple Machine Learning Research

Paper Index on ACL Anthology

A Bounded Coordination-Support Capability for Multi-Party Settings: Task-State Monitoring in Firefighter Incident Command A Dataset of Latin Etymologies Extracted from Wiktionary An Efficient Approach for Answering Not Readily Attainable Questions for RAG-based Applications Automated German Alt Text Generation for News Charts Call Support Copilot: A Reproducible Multimodal System for Speech Emotion Recognition, Intent Understanding, and Agent Assistance Can Large Language Models Replace Statistical Software? Code-Switching Detection in Multilingual Child Speech with SwissBERT Concept Extraction and Webb’s Depth of Knowledge: Comparing LLM Question Generation Pipelines for Educational Assessment Data Augmentation for Historical NER: A Systematic Comparison of Lexical and LLM-based Approaches Enhancing Retrieval via Cognitively Motivated Document Expansion Extending the Contact Hypothesis: Cross-Linguistic Evaluation of Religion and Nationality Bias When Prompting LLMs in German and Icelandic Extracting Article-Level Legal Dependencies from Swiss Federal Law using LLMs How Good is AI on Swiss Voting Booklets? A Multilingual OCR and Alignment Benchmark Optimizing Large Language Models for Robust Domain-Specific Text-to-SQL: From Prompting to Preference Alignment Proceedings of the 11th Edition of the Swiss Text Analytics Conference Reinforcement Learning for Latent-Space Thinking in LLMs RUMLEM: A Dictionary-Based Lemmatizer for Romansh Skill Extraction from Resumes and Job Offers across Six Languages Text vs. Phoneme Intermediates for Low-Resource Swiss German The Same Email, Signed Differently: Testing Negotiation Bias and Recommendation Stability in LLMs Which Skills Debate Reaches the Public? Comparing Scientific Literature and Media Coverage of AI and LLM Skill Impacts (2022–2025) Controlling Language and Style of Multi-lingual Generative Language Models with Control Vectors Hybrid Human-LLM Corpus Construction and LLM Evaluation for the Caused-Motion Construction Implicit and Indirect: Detecting Face-threatening and Paired Actions in Asynchronous Online Conversations Northern European Journal of Language Technology, Volume 11 A modular architecture for creating multimodal embodied agents with an episodic Knowledge Graph as an explainable and controllable long-term memory A Neural Approach to Discourse Relation Signal Detection An Analysis of Japanese Sentence-final Particle Yone: Compare Yone and Ne in Response Attribution and the discourse structure of reports Automatic Detection of the Bulgarian Evidential Renarrative
Self-Repair in Tigrinya: Trouble Sources, Mechanisms and ...
2026-04-20 · via Paper Index on ACL Anthology

Abstract

This paper analyzes conversational self-repair, which refers to reconstructing problematic portions of a prior oral discourse by oneself, in Tigrinya. Tigrinya is a Northern Ethio-Eritrean-Semitic language spoken by the inhabitants of the Tigray regional state of Ethiopia and Eritrea. This article relies on recorded oral data from speakers of the Rayya Tigrinya variety, particularly inhabitants of Neksege located to the West of Maichew. A conversational analysis (CA) approach is used to analyze the trouble sources, mechanisms (initiators), and results of self-repair. The article shows that pronunciation problems emanating from dialectal variation or tongue slip, wrong word order including focus misplacement, missing constituents, perceived misunderstandings, and using (totally) wrong constituents are some of the trouble sources that push speakers to repair portions of a prior oral utterance. On top of that, cut-offs, particles, and lexemes (one verbal noun and some predicates) are identified as self-repair initiators. Though cut-offs do not indicate a self-repair, particles and predicates may sometimes indicate a self-repair. Finally, the article posits that expanding, replacing, re-ordering, aborting and restarting, and inserting are some of the solutions set for the repairable segments in the repaired portions of the oral discourse. The author recommends for further investigation repair in the process of language acquisition and learning, and the relationship between self-repair and the demographic features of participants.

Anthology ID:
2024.dnd-15.2
Volume:
Dialogue & Discourse Volume 15
Month:
October
Year:
2024
Address:
Chicago, Illinois, USA
Editors:
Junyi Jessy Li, Manfred Stede, Amir Zeldes, Jonathan Ginzburg, Kallirroi Georgila, David Traum
Venue:
DND
SIG:
SIGDIAL
Publisher:
University of Illinois Chicago
Note:
Pages:
85–112
Language:
URL:
https://aclanthology.org/2024.dnd-15.2/
DOI:
10.5210/dad.2024.203
Bibkey:
Cite (ACL):
Dagnew Mache Asgede. 2024. Self-Repair in Tigrinya: Trouble Sources, Mechanisms and Solutions. Dialogue & Discourse, 15:85–112.
Cite (Informal):
Self-Repair in Tigrinya: Trouble Sources, Mechanisms and Solutions (Asgede, DND 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.dnd-15.2.pdf