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Self-Repair in Tigrinya: Trouble Sources, Mechanisms and Solutions

Paper · 2024 · Mache Asgede, Dagnew

Summary

Self-repair, where speakers revise parts of their own earlier talk, has barely been described for Tigrinya or for Ethiopian languages generally. This paper starts to fill that gap. Using Conversation Analysis, the author studied spoken material from the Rayya variety, recorded during a 2019 field trip to Neksege in Southern Tigray. The material covers informal interviews, spontaneous conversation and tales. Audio was prepared in Audacity and then segmented in ELAN into turns, sentences and clauses. Four raters labelled the repairs, and their agreement was checked with Fleiss' kappa. The study sorts each repair along three axes:

  • Trigger: pronunciation, word choice, missing or wrong constituents, ordering and focus errors, misunderstandings.
  • Signal: cut-offs, particles, a few predicates and one verbal noun.
  • Fix: expanding, replacing, reordering, abandoning and restarting, inserting.

About 400 repairs were identified, with very high rater agreement. The work gives a first descriptive baseline for self-correction in an under-resourced Semitic language. It also shows that contact between local dialect and the quasi-standard norm is a major driver of repair.

Key points

  • The corpus consists of recordings from Neksege (Rayya Tigrinya), collected April–July 2019, processed in Audacity and annotated in ELAN. It contains 400 repairs, labelled by four raters with Fleiss' kappa 0.835.
  • Trouble sources ranked by share:
    • pronunciation 0.218
    • diction 0.173
    • constituents left out 0.159
    • incorrect constituents 0.125
    • ordering of words 0.124
    • misunderstanding 0.106
    • focus misplacement 0.088
  • Cut-offs account for the largest share of self-repairs (0.593). However, they also serve other purposes, mainly speech planning, so on their own they do not reliably mark a repair.
  • Different lexical initiators tend to go with different operations:
    • The particle ʔɨbba 'I mean' and the predicates ʔajχonχuj 'I am not' and ʔɨjʔoj 'no' introduce replacement; ʔɨbba also appears with reordering.
    • The particle dəʔa 'rather' introduces insertion.
    • The verbal noun malət 'means' introduces expansion.
  • Some repairs had social causes. Speakers shifted words toward the standard form because the researcher was present, and they changed verbs to honorific forms to suit the addressee's status.

Interesting ideas

  • I1. The paper splits each repair into three separate labels: why it happened, what signalled it, and what operation fixed it. This keeps cause, cue and edit apart instead of lumping them into one "disfluency" tag.
  • I2. Specific markers predict specific edits: "I mean"-type markers lead to replacement, "rather" to insertion, "means" to expansion. A cue word thus tells you how the earlier material should be revised, not just that a correction is coming.
  • I3. The paper finds that the most frequent surface cue (cut-offs) is ambiguous, because it also marks planning. A common signal therefore should not be treated as proof of the phenomenon.
  • I4. Repair is not always fixing an error; speakers also use it to clarify or rephrase. So a revised phrase may enrich the earlier one rather than cancel it.
  • I5. Several annotators label the data independently, and their agreement is reported as a kappa before any frequencies are trusted.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): self-induced repair rules could be logged with the same three fields (trigger, cue, edit type), making them easier to group, audit and explain.
  • I2 (world): when English text contains "I mean", "rather" or "that is", the world builder could replace, add to or elaborate the earlier entity or state, depending on the marker.
  • I3 (learn): evaluations of correction detection should include negative controls where the same cue appears without any correction, to measure false triggers.
  • I4 (frames): a correction-like speech act should be split into retraction and elaboration, so the earlier proposition is either withdrawn or kept and refined.
  • I5 (learn): silver data from witness parsers or LLM teachers could carry a similar chance-corrected agreement score, so that only well-agreed labels are used for training.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

Also on the byline: Arba Minch University.

📚 Shelves 3The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 worksPhilosophy · 211 works

🧭 Topics 1Research subjects and languages the work is about.Semitic languages · 1,484 works

Cite

Cite as Mache Asgede, Dagnew. 2024. Self-Repair in Tigrinya: Trouble Sources, Mechanisms and Solutions. Dialogue & Discourse.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.5210/dad.2024.203

@inproceedings{dagnew2024self,
  title = {{Self-Repair in Tigrinya: Trouble Sources, Mechanisms and Solutions}},
  author = {Mache Asgede, Dagnew},
  booktitle = {Dialogue \& Discourse},
  year = {2024},
  doi = {10.5210/dad.2024.203},
  url = {https://doi.org/10.5210/dad.2024.203}
}

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