Detection of Reformulations in Spoken French

Natalia Grabar, Iris Eshkol-Taravela


Abstract
Our work addresses automatic detection of enunciations and segments with reformulations in French spoken corpora. The proposed approach is syntagmatic. It is based on reformulation markers and specificities of spoken language. The reference data are built manually and have gone through consensus. Automatic methods, based on rules and CRF machine learning, are proposed in order to detect the enunciations and segments that contain reformulations. With the CRF models, different features are exploited within a window of various sizes. Detection of enunciations with reformulations shows up to 0.66 precision. The tests performed for the detection of reformulated segments indicate that the task remains difficult. The best average performance values reach up to 0.65 F-measure, 0.75 precision, and 0.63 recall. We have several perspectives to this work for improving the detection of reformulated segments and for studying the data from other points of view.
Anthology ID:
L16-1596
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3760–3767
Language:
URL:
https://aclanthology.org/L16-1596
DOI:
Bibkey:
Cite (ACL):
Natalia Grabar and Iris Eshkol-Taravela. 2016. Detection of Reformulations in Spoken French. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3760–3767, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Detection of Reformulations in Spoken French (Grabar & Eshkol-Taravela, LREC 2016)
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PDF:
https://aclanthology.org/L16-1596.pdf