DialQA

The Dialectal Extractive Question Answering Shared Task invites participants to build QA systems that are robust to dialectal variation. The task builds on existing QA benchmarks TyDi-QA and SD-QA): specifically, it uses portions of the SD-QA dataset, which recorded dialectal variations of TyDi-QA questions. The participants may either (a) use the baseline automatic speech recognition outputs for each dialect with the aim of making a robust text-based QA system, or (b) they may use the provided audio recordings of the questions with the aim of making a dialect-robust ASR system which can be then evaluated with a baseline QA system, or (c) both of the above. The shared task provides development and test data for 5 varieties of English (Nigeria, USA, South India, Australia, Philippines), 4 varieties of Arabic (Algeria, Egypt, Jordan, Tunisia), and 2 varieties of Kiswahili (Kenya, Tanzania), as well as code for training baseline systems with modified TyDi-QA data. Any training data are allowed, except for the TyDi-QA data in the above 3 languages.

Requirements and installation

./install.sh

Data File Structure

data/
	dialqa-train.json
	dialqa-dev-og.json
	dialqa-dev-aug.json
	audio/
		dev/
			{lang}/
				{dialect-region}/
					{lang}-{id}-{dialect-region}.wav

dialqa-dev-og.json: Original Development dataset gold questions.
dialqa-dev-aug.json: Development dataset with dialectal questions (speech-to-text outputs through automatic ASR). This is our task development dataset.
lang: English (eng), Arabic (ara), Kiswahili (swa)
audio: folder containing question audio files. The audio file names {lang}-{id}-{dialect-region} have one-to-one mappings with the example ids from the json files.

Baseline (ASR QA)

The task is to perform Extractive-QA using dialectal questions (Speech to text Outputs). We use the Google Speech API with regional units (eg. en-US, sw-TZ) to perform speech to text conversion. The training file is based on huggingface's [run_squad.py] file.

Training baseline:


source vdial/bin/activate


python src/run_squad.py \
	--model_type bert \
	--model_name_or_path=bert-base-multilingual-uncased \
	--do_train \
	--do_lower_case \
	--train_file 'data/dialqa-train.json' \
	--per_gpu_train_batch_size 16 \
	--per_gpu_eval_batch_size 24 \
	--learning_rate 3e-5 \
	--num_train_epochs 3 \
	--max_seq_length 384 \
	--doc_stride 128 \
	--output_dir 'train_cache_output/' \
	--overwrite_cache \
	--overwrite_output_dir

Prediction on augmented dev data

python src/run_squad.py \
	--model_type bert \
	--model_name_or_path='train_cache_output' \
	--do_eval \
	--do_lower_case \
	--predict_file 'data/dialqa-dev-aug.json' \
	--per_gpu_train_batch_size 16 \
	--per_gpu_eval_batch_size 16 \
	--learning_rate 3e-5 \
	--num_train_epochs 3 \
	--max_seq_length 384 \
	--doc_stride 128 \
	--output_dir 'outputs/aug-mbert' \
	--overwrite_output_dir

Prediction on test data

python src/run_squad.py \
	--model_type bert \
	--model_name_or_path='train_cache_output' \
	--do_eval \
	--do_lower_case \
	--predict_file 'data/dialqa-test.json' \
	--per_gpu_train_batch_size 16 \
	--per_gpu_eval_batch_size 16 \
	--learning_rate 3e-5 \
	--num_train_epochs 3 \
	--max_seq_length 384 \
	--doc_stride 128 \
	--output_dir 'outputs/test-mbert' \
	--overwrite_output_dir

Baseline results

Language-Dialect	F1	Exact Match	Example Count
English-Nigeria (nga)	73.36	58.70	494
English-United States (usa)	74.35	59.31	494
English-South India (ind_s)	72.22	58.10	494
English-Australia (aus)	73.67	59.52	494
English-Philippines (phl)	73.76	59.11	494
English-Dialect (avg)	73.47	58.95	2470

Arabic-Algeria (dza)	71.72	56.17	324
Arabic-Egypt (egy)	72.39	56.79	324
Arabic-Jordan (jor)	73.27	57.41	324
Arabic-Tunisia (tun)	73.55	57.71	324
Arabic-Dialect (avg)	72.73	57.02	1296

Kiswahili-Kenya (ken)	72.12	63.1	1000
Kiswahili-Tanzania (tza)	70.74	61.7	1000
Kiswahili-Dialect (avg)	71.43	62.4	2000

All Language (avg)	72.60	59.71	5766

Citation

Audio files and augmented dataset are from SD-QA which was built on top of TyDiQA.

@inproceedings{faisal-etal-2021-sd-qa,
    title = "{SD}-{QA}: Spoken Dialectal Question Answering for the Real World",
    author = "Faisal, Fahim  and
      Keshava, Sharlina  and
      Alam, Md Mahfuz Ibn  and
      Anastasopoulos, Antonios",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-emnlp.281",
    doi = "10.18653/v1/2021.findings-emnlp.281",
    pages = "3296--3315",
}

@article{clark-etal-2020-tydi,
    title = "{T}y{D}i {QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages",
    author = "Clark, Jonathan H.  and
      Choi, Eunsol  and
      Collins, Michael  and
      Garrette, Dan  and
      Kwiatkowski, Tom  and
      Nikolaev, Vitaly  and
      Palomaki, Jennimaria",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/2020.tacl-1.30",
    doi = "10.1162/tacl_a_00317",
    pages = "454--470",
}

License

The data is availalbe under the Apache License 2.0.

ffaisal93/DialQA