|
|
--- |
|
|
dataset_info: |
|
|
features: |
|
|
- name: language |
|
|
dtype: string |
|
|
- name: text |
|
|
sequence: string |
|
|
- name: label |
|
|
sequence: string |
|
|
splits: |
|
|
- name: train |
|
|
num_bytes: 8854308 |
|
|
num_examples: 24453 |
|
|
- name: test |
|
|
num_bytes: 1053019 |
|
|
num_examples: 2711 |
|
|
download_size: 1806180 |
|
|
dataset_size: 9907327 |
|
|
configs: |
|
|
- config_name: default |
|
|
data_files: |
|
|
- split: train |
|
|
path: data/train-* |
|
|
- split: test |
|
|
path: data/test-* |
|
|
language: |
|
|
- hi |
|
|
- te |
|
|
- ta |
|
|
- mr |
|
|
- bn |
|
|
- kn |
|
|
--- |
|
|
# Cite |
|
|
|
|
|
``` |
|
|
@inproceedings{kochar-etal-2024-towards, |
|
|
title = "Towards Disfluency Annotated Corpora for {I}ndian Languages", |
|
|
author = "Kochar, Chayan and |
|
|
Mujadia, Vandan Vasantlal and |
|
|
Mishra, Pruthwik and |
|
|
Sharma, Dipti Misra", |
|
|
editor = "Jha, Girish Nath and |
|
|
L., Sobha and |
|
|
Bali, Kalika and |
|
|
Ojha, Atul Kr.", |
|
|
booktitle = "Proceedings of the 7th Workshop on Indian Language Data: Resources and Evaluation", |
|
|
month = may, |
|
|
year = "2024", |
|
|
address = "Torino, Italia", |
|
|
publisher = "ELRA and ICCL", |
|
|
url = "https://aclanthology.org/2024.wildre-1.1/", |
|
|
pages = "1--10", |
|
|
abstract = "In the natural course of spoken language, individuals often engage in thinking and self-correction during speech production. These instances of interruption or correction are commonly referred to as disfluencies. When preparing data for subsequent downstream NLP tasks, these linguistic elements can be systematically removed, or handled as required, to enhance data quality. In this study, we present a comprehensive research on disfluencies in Indian languages. Our approach involves not only annotating real-world conversation transcripts but also conducting a detailed analysis of linguistic nuances inherent to Indian languages that are necessary to consider during annotation. Additionally, we introduce a robust algorithm for the synthetic generation of disfluent data. This algorithm aims to facilitate more effective model training for the identification of disfluencies in real-world conversations, thereby contributing to the advancement of disfluency research in Indian languages." |
|
|
} |
|
|
``` |
