Benchmarking Performance in LowResource Translation Results for African
Benchmarking Performance in Low-Resource Translation: Results for African & Indian Languages Dr. Kevin Duh Dr. Matt Post JHU HLTCOE Dr. Paul Mc. Namee
Research Question for this talk How does state-of-the-art Neural Machine Translation (NMT) compare with Statistical Machine Translation (SMT) for low-resource scenarios? Evaluate on African & Indian languages - Little bilingual text (bitext) MT is challenging to develop - Fewer linguists w. r. t major languages MT has potential for impact 1
How Data Size Impacts SMT/NMT Performance Figure from JHU’s Philipp Koehn: 2017 result (RNN+attention NMT) Better Translation 2
How Data Size Impacts SMT/NMT Performance Research Progress in NMT Figure from JHU’s Philipp Koehn: 2017 result (RNN+attention NMT) Better Translation 3
Outline 1. 2. 3. 4. Motivation: Importance of Low-Resource Brief Explanation of Neural Machine Translation (NMT) African languages Benchmark Indian languages Benchmark 4
Training data: Bilingual Text (Bitext) 5
Sentence Embedding Word Embedding 6
Translate word-by-word with a recurrent neural net (RNN) 7
Translate word-by-word with a recurrent neural net (RNN) 8
State-of-the-art NMT 2014: RNN Encoder-Decoder 2015 -2016: RNN+Attention Model 2017 -2019: Transformer Model We’ll focus on Transformers 9
Outline 1. 2. 3. 4. Motivation: Importance of Low-Resource Brief Explanation of Neural Machine Translation (NMT) African languages Benchmark Indian languages Benchmark 10
Somali Swahili From: https: //en. wikipedia. org/wiki/Lang uages_of_Africa 11
Experiment Setup (version 1) Compare best SMT and best NMT performance on: - Somali-to-English - Swahili-to-English Bitext from IARPA MATERIAL: - Only 24 k sentences for training (~800 k words) - Focus on text. Matched condition (unlike SCALE’ 18) 12
To find the best NMT, we trained ~600 models with different hyperparameters Subword units Word Embedding size Neural Layers & Architecture Training algorithm details Etc, …. 13
Take-home message #1: NMT is competitive w/ SMT, but requires careful hyperparameter tuning Histogram of BLEU scores for the 600 Swahili-English NMT models Median NMT: 18. 7 BLEU SMT: 24. 4 BLEU Frequency Best NMT: 24. 8 BLEU better translation 14
Take-home message #1: NMT is competitive w/ SMT, but requires careful hyperparameter tuning Histogram of BLEU scores for the 600 Somali-English NMT models Median NMT: 11. 7 BLEU SMT: 15. 1 BLEU Frequency Best NMT: 14. 4 BLEU better translation 15
Importance of hyperparameter tuning for NMT There exist thousands of hyperparameter combinations! Best practice: - We know general trends, e. g. smaller subwords for low-resource - We know reasonable ranges, e. g. #layer = 2 -8 - Try as many combinations as possible within these ranges Burgeoning research field: - Hyperparameter Optimization (HPO), aka Auto. ML 16
Experiment Setup (version 2) How do SMT & NMT compare if we exploit alternative data? Bilingual Dictionary (words, not sentences) Clean Bitext from IARPA MATERIAL (Experiment 1) Found Bitext (different domain/genre) Mined Bitext, i. e. Paracrawl (noisy) 17
Take-home message #2: Exploiting alternative 28 data improves performance, especially for NMT 26 24 22 Somali-English Result SMT 20 18 NMT 16 BLEU: Better 14 Translation 12 10 Clean Bitext (24 k) Add Found Add Mined Dictionary Bitext (24 k+26 k=50 k) (50 k+220 k=270 k) (270 k+80 k=350 k) Rule of thumb for interpreting BLEU: 15%: useful for triage 25%: useful gists >40%: nearly fluent 18
Somali-English models 35 BLEU (better) IARPA MATERIAL Testset BLEU (better) 25 20 15 Swahili-English models 30 25 (matched condition) (additional evaluation) Clean Bitext (24 k) Add Dict (50 k) +Found Bitext (270 k) 20 +Mined Bitext (250 k) 15 20 10 15 5 0 BLEU (better) Open. Source. gov Testset BLEU (better) 10 Clean Bitext (24 k) Add Dict (120 k) +Found Bitext (310 k) +Mined Bitext (370 k) 10 5 19
Somali-English Examples 20
Swahili-English Examples 21
What about other African languages? Different languages in Africa have available different kinds of resources Requires a multitude of MT solutions Note: This table is meant to illustrate the diversity in available resources. It does not represent our list of “priority” languages Language Estimated size of monolingual text (# webpages) Estimated size of found bitext (# sentences) Arabic 17 million 70 million Chewa 8 thousand 900 thousand Hausa 45 thousand 400 thousand Igbo 8 thousand 500 thousand Fulani nonexistent? 3 hundred Malagasy 126 thousand 900 thousand Oromo 15 thousand 200 thousand Somali 117 thousand 200 thousand Swahili 234 thousand 1. 2 million 22
Outline 1. 2. 3. 4. Motivation: Importance of Low-Resource Brief Explanation of Neural Machine Translation (NMT) African languages Benchmark Indian languages Benchmark 23
Lo. Res. MT'19 workshop at MT Summit Shared task in 4 languages (3 Indian + Latvian) Bhojpuri, Magahi, Sindhi Serendipitously at the same time as African experiments 24
Sindhi Bhojpuri Magahi From: https: //en. wikipedia. org/wiki/Lang uages_of_India 25
35 Comparison of State-of-the-art SMT & NMT BLEU (better) 30 25 20 SMT 15 NMT Top 10 5 0 Bhojpuri-English Magahi-English Sindhi-English (bho-eng) (mag-eng) (snd-eng) 29 k sents 4 k sents 29 k sents 26
Future Work in Low-Resource MT Better models and training algorithms - Morphological and syntactic models - Robust training objectives, unsupervised objectives - Synthetic Data Augmentation (next slide) Better mined bitext - (next talk) 27
Future Work in Low-Resource MT: Synthetic Data Augmentation Backtranslation: effective method in high-resource settings (+3 BLEU), but difficult for low-resource (-3 BLEU) snd English Sindhi Training Bitext snd Sindhi English Synthetic Bitext 28
Future Work in Low-Resource MT: Synthetic Data Augmentation Paraphrase Augmentation: new method that exploits ability to rephrase English sentences (suitable for low-resource) English Paraphraser eng Training Bitext eng Synthetic Bitext Sindhi English 29
Conclusions • Benchmark SMT vs NMT in Low-Resource languages • Take-Home #1: NMT is competitive with SMT, but NMT hyperparameter tuning is essential • Take-Home #2: NMT benefits more from additional data, so improving data pipeline is an important research goal 30
Questions? Comments? asante (Swahili) mahadsanid (Somali) dhanvaad (Bhojpuri) dhenjewaad (Magahi) mehrbani (Sindhi)
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Progress in Machine Translation (MT) Seminal SMT paper from IBM Warren Weaver’s memo 1947 Founding of SYSTRAN. Development of Rulebased MT (RBMT) 1968 2011 -2012: Early deep learning success in speech/vision 2015: Seminal NMT paper (RNN+attention) 2016: Google announces NMT in production 2017: New NMT architecture: Transformer DARPA TIDES, GALE, BOLT programs Open-source of Moses toolkit Development of Statistical MT (SMT) 1993 Early 2000 s 2010 s-Present 33
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Different languages in Africa have available different kinds of resources Requires a multitude of MT solutions
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