Report on the 11th IWSLT evaluation campaign
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Summary
This report describes the 2014 (11th) evaluation campaign of the IWSLT workshop. The campaign benchmarks the core technologies of spoken language translation on TED and TEDx lectures. Three task families were offered:
- ASR: speech-to-transcript in English, German and Italian, with Italian new this year.
- SLT: speech-to-foreign-text.
- MT: translation of clean transcripts.
The official SLT and MT directions paired English with French, German and Italian. Optional directions involved 12 further languages. Organizers supplied training and development data, mainly WIT 3 TED material plus WMT 2014, MultiUN and SETimes corpora. Each test period lasted about a week. All submissions were scored automatically: WER for ASR, BLEU/TER for translation. Teams also ran their systems on the 2013 test set so that year-over-year progress could be measured. For English-French and English-German MT, professional translators post-edited system outputs, and HTER was computed from these post-edits. Twenty-one teams took part. English ASR improved clearly over 2013, while TEDx German audio remained hard. The campaign provides a shared multilingual benchmark for lecture translation and tracks progress over time.
Key points
- 21 participating sites submitted 76 primary runs: 15 ASR, 16 SLT and 45 MT.
- Primary ASR WER on tst2014 ranged from 8.4–19.7% for English, 24.0–38.8% for German and 21.9–25.4% for Italian.
- Two German TEDx talks exceeded 40% WER because of poorer recordings.
- On tst2013, the best English WER fell from 13.5% to 10.6%, about a 21% relative gain in one year.
- The organizers' ROVER combination of five English systems, used as SLT input, reached 8.2% WER.
- Supplied resources included the WIT 3 TED corpus, WMT 2014 data, MultiUN, SETimes and LDC monolingual corpora.
- Human evaluation used HTER on professional post-edits for the English-French and English-German MT tracks.
Cited by
- Farasa: A Fast and Furious Segmenter for Arabic
- An Actor-Critic Algorithm for Sequence Prediction
- Reinforced Self-Training (ReST) for Language Modeling
- Large-Scale Machine Translation between Arabic and Hebrew: Available Corpora and Initial Results
- Sequence Level Training with Recurrent Neural Networks
Cite
@inproceedings{cettolo2014report,
title = {{Report on the 11th IWSLT evaluation campaign}},
author = {Cettolo, Mauro and Niehues, Jan and Stüker, Sebastian and Bentivogli, Luisa and Federico, Marcello},
booktitle = {Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign},
year = {2014},
url = {https://aclanthology.org/2014.iwslt-evaluation.1/}
}References
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