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LanguageRelatednessandLexicalClosenesscanhelpImprove
Multilingual NMT: IITBombay@MultiIndicNMTWAT2021
Jyotsana Khatri, Nikhil Saini, Pushpak Bhattacharyya
Department of Computer Science and Engineering
Indian Institute of Technology Bombay
Mumbai,India
{jyotsanak, nikhilra, pb}@cse.iitb.ac.in
Abstract In this paper, we present our system for Multi-
Multilingual Neural Machine Translation has IndicMT: An Indic Language Multilingual Task at
achieved remarkable performance by training WAT2021(Nakazawaetal.,2021). Thetaskcovers
a single translation model for multiple lan- 10 Indic Languages (Bengali, Gujarati, Hindi, Kan-
guages. This paper describes our submission nada, Malayalam, Marathi, Oriya, Punjabi, Tamil,
(TeamID:CFILT-IITB)fortheMultiIndicMT: and Telugu) and English.
AnIndic Language Multilingual Task at WAT To summarize our approach and contributions,
2021. We train multilingual NMT systems by we (i) present a multilingual NMT system with
sharing encoder and decoder parameters with shared encoder-decoder framework, (ii) show re-
language embedding associated with each to- sults on many-to-one translation, (iii) use transliter-
ken in both encoder and decoder. Further- ation to a common script to handle the lexical gap
more, we demonstrate the use of translitera- between languages, (iv) show how grouping of lan-
tion (script conversion) for Indic languages in guagesinregardtotheirlanguagefamilyhelpsmul-
reducing the lexical gap for training a multilin-
gual NMTsystem. Further, we show improve- tilingual NMT and (v) use language embeddings
mentinperformancebytrainingamultilingual with each token in both encoder and decoder.
NMTsystemusinglanguagesofthesamefam-
ily, i.e., related languages. 2 Related work
1 Introduction 2.1 Neural Machine Translation
NeuralMachineTranslation(Sutskeveretal.,2014; Neural Machine Translation architectures consist
Bahdanauetal., 2015; Wu et al., 2016) has become of encoder layers, attention layers, and decoder lay-
a de-facto for automatic translation of language ers. NMT framework takes a sequence of words
pairs. NMT systems with Transformer (Vaswani as an input; the encoder generates an intermediate
et al., 2017) based architectures have achieved com- representation, conditioned on which, the decoder
petitive accuracy on data-rich language pairs like generates an output sequence. The decoder also at-
English-French. However, NMT systems are data- tends to the encoder states. Bahdanau et al. (2015)
hungry, and only a few pairs of languages have introduced the encoder-decoder attention to allow
abundant parallel data. For low resource setting, the decoder to soft-search the parts of the source
techniques like transfer learning (Zoph et al., 2016) sentence to predict the next token. The encoder-
and utilization of monolingual data in an unsuper- decoder can be a LSTM framework (Sutskever
vised setting (Artetxe et al., 2018; Lample et al., et al., 2014; Wu et al., 2016), CNN (Gehring et al.,
2017, 2018) have shown support for increasing 2017), or Transformer layers (Vaswani et al., 2017).
the translation accuracy. Multilingual Neural Ma- ATransformer layer comprises of self-attention
chine Translation is an ideal setting for low re- that bakes the understanding of input sequence with
source MT (Lakew et al., 2018) since it allows positional encoding and passes on to the next com-
sharing of encoder-decoder parameters, word em- ponent, feed-forward neural network, layer normal-
beddings, and joint or separate vocabularies. It ization, and residual connections. The decoder in
also enables zero-shot translations, i.e., translating the transformer has an additional encoder-attention
between language pairs that were not seen during layer that attends to the output states of the trans-
training (Johnson et al., 2017a). former encoder.
217
Proceedings of the 8th Workshop on Asian Translation, pages 217–223
Bangkok, Thailand (online), August 5-6, 2021. ©2021 Association for Computational Linguistics
NMTisdata-hungry,andonlyafewpairsoflan- 2.3 LanguageRelatedness
guages have abundant parallel data. In recent years, Telugu, Tamil, Kannada, and Malayalam are Dra-
NMThasbeenaccompaniedbyseveraltechniques vidian languages whose speakers are predomi-
to improve the performance of both low & high nantly found in South India, with some speakers in
resource language pairs. Back-translation (Sen- Sri Lanka and a few pockets of speakers in North
nrich et al., 2016b) is used to augment the paral- India. The speakers of these languages constitute
lel data with synthetically generated parallel data around 20% of the Indian population (Kunchukut-
bypassing monolingual datasets to the previously tan and Bhattacharyya, 2020). Dravidian languages
trained models. Currently, NMT systems also per- are agglutinative, i.e., long and complex words are
form on-the-fly back-translation to train the model formed by stringing together morphemes without
simultaneously. Tokenization methods like Byte changing them in spelling or phonetics. Most Dra-
Pair Encoding (Sennrich et al., 2016a) are used in vidian languages have clusivity distinction. Hindi,
almost all NMT models. Pivoting (Cheng et al., Bengali, Marathi, Gujarati, Oriya, Punjabi are Indo-
2017) and Transfer Learning (Zoph et al., 2016) AryanlanguagesandareprimarilyspokeninNorth
have leveraged the language relatedness by indi- and Central India and the neighboring countries
rectly providing the model with more parallel data of Pakistan, Nepal, and Bangladesh. The speakers
from related language pairs. of these languages constitute around 75% of the
Indian population. Both Dravidian and Indo-Aryan
2.2 Multilingual Neural Machine Translation language families follow the Subject(S)-Object(O)-
Verb(V) order.
Multilingual NMT trains a single model utilizing Grouping languages concerning their families
data from multiple language-pairs to improve the have inherent advantages because they form a
performance. There are different approaches to closely related group with several linguistic phe-
incorporate multiple language pairs in a single nomenonssharedamongstthem. Indo-Aryan lan-
system, like multi-way NMT, pivot-based NMT, guages are morphologically rich and have huge
transfer learning, multi-source NMT and, multi- similarities when compared to English. A language
lingual NMT (Dabre et al., 2020). Multilingual group also share vocabularies at both word and
NMTcameintopicture because many languages character level. They contain similarly spelled
share certain amount of vocabulary and share some words that are derived from the same root. ‘
structural similarity. These languages together can 2.4 Transliteration
be utilized to improve the performance of NMT Indic languages share a lot of vocabulary, but most
systems. In this paper, our focus is to analyze the languages utilize different scripts. Nevertheless,
performance of multi-source NMT. The simplest these scripts have phoneme overlap and can be
approach is to share the parameters of NMT model converted easily from one to another using a simple
across multiple language pairs. These kinds of sys- rule-based system. To convert all Indic language
tems work better if languages are related to each 1
other. In Johnson et al. (2017b), the encoder, de- data into the same script, we use IndicNLP which
coder, and attention are shared for the training of maps different Unicode range for the conversion.
multiple language pairs and a target language to- Theconversion of all Indic language scripts to the
ken is added at the beginning of target sentence same script helps with better shared vocabulary
while decoding. Firat et al. (2016) utilizes a shared and leads to smaller subword vocabulary (Ramesh
attention mechanism to train multilingual models. et al., 2021).
Recently many approaches have been proposed, 3 Systemoverview
where monolingual data of multiple languages is In this section, we describe the details of the sub-
utilized to pre-train a single model using different mitted systems to MultiIndicMT task at WAT2021.
objectives like masked language modeling and de- Wereport results for four types of models:
noising (Lample and Conneau, 2019; Song et al.,
2019; Lewis et al., 2020; Liu et al., 2020). Multi- • Bilingual: Trainedonlyusingparalleldatafor
lingual pre-training followed by multilingual fine- a particular language pair (bilingual models).
tuning has also proven to be beneficial (Tang et al., 1https://github.com/anoopkunchukuttan/
2020). indic_nlp_library
218
• All-En: Multilingual many-to-one system of all languages into the same script, hence the
trained using all available parallel data of all choice of Devnagari as a common script is arbi-
3
language pairs. trary. We use fastBPE to learn BPE (Byte pair
• IA-En: Multilingual many-to-one system encoding) (Bojanowski et al., 2017). For bilin-
trained using Indo-Aryan languages from the gual models, we use 60000 BPE codes over the
provided parallel data. combined tokenized data of both languages. The
numberofBPEcodesissetto100000forAll-En,
• DR-En: Multilingual many-to-one system and 80000 for DR-En and IA-En.
trained using Dravidian languages from the 4.3 Experimental Setup
provided parallel data.
Weusesixlayers in the encoder, six layers in the
Totrain our multilingual models, we use shared decoder, 8 attention heads in both encoder and de-
encoder-decoder transformer architecture. To han- coder, and 1024 embedding dimension. The en-
dle the lexical gap between Indic languages in mul- coderanddecoderaretrainedusingAdam(Kingma
tilingual models, we convert the data of all Indic and Ba, 2015) optimizer with inverse square root
languages to a common script. We choose the learning rate schedule. We use the same setting
common script as Devanagari (arbitrary choice). as used in Song et al. (2019) for warmup phase,
Wealso perform a comparative study of systems in which the learning rate is increased linearly for
whentheencoderanddecoderaresharedonlybe- −
some initial steps starting from 1e 7 to 0.0001,
tween related languages. To perform this com- warmup phase is set to 4000 steps. We use mini-
parative study, we group the provided set of lan- batches of size 2000 tokens and set the dropout
guages in two parts based on the language families to 0.1 (Gal and Ghahramani, 2016). Maximum
they belong to, i.e, the system is trained from Indo- sentence length is set to 100 after applying BPE.
Aryan (group) to English, and Dravidian (group) At decoding time, we use greedy decoding. For
to English. Indo-Aryan-to-English contains Ben- 4
experiments, we are using mt steps from MASS
gali, Gujarati, Hindi, Marathi, Oriya, Punjabi to codebase. Our models are trained using only par-
English, and Dravidian-to-English contains Kan- allel data provided in the task, we are not training
nada, Malayalam, Tamil, Telugu to English. We the model using any kind of pretraining objective.
use shared subword vocabulary of the languages Wetrain bilingual models for 100 epochs and mul-
involved while training multilingual models, and a tilingual models for 150 epochs. The epoch size
commonvocabularyofsourceandtargetlanguages is set to 200000 sentences. Due to resource con-
to train bilingual models. straints, we train our model for fixed number of
4 Experimental details epochs, it does not guarantee convergence. Similar
to MASS(Songetal.,2019),languageembeddings
4.1 Dataset are added to each token in the encoder and decoder
Ourmodelsaretrained using only the parallel data to distinguish between languages. These language
provided for the task. The size of the parallel data embeddings are learnt during training.
available and its source of origin are summarized 4.4 Results and Discussion
in Table 1. The validation and test data provided in Wereport BLEUscores for our four settings: bilin-
the task is n-way and contains 1000 sentences for gual, All-En (multilingual many-to-one), IA-En
validation and 2390 sentences in test set. (multilingual many-to-one Indo-Aryan to English),
4.2 Datapreprocessing and DR-En (multilingual many-to-one Dravidian
to English) in Table 2. We use multi-bleu.perl 5 to
We tokenize English language data using moses calculate BLEU scores of baseline models. BLEU
tokenizer (Koehn et al., 2007), and Indian language score is calculated using the tokenized reference
data using IndicNLP2 library. For multilingual
models, we transliterate (script mapping) all In- and hypothesis files as followed by organizers in
dic language data into Devanagari script using the 3https://github.com/glample/fastBPE
IndicNLPlibrary. Our aim here is to convert data 4https://github.com/microsoft/MASS
5https://github.com/moses-smt/
2https://github.com/anoopkunchukuttan/ mosesdecoder/blob/RELEASE-2.1.1/scripts/
indic_nlp_library generic/multi-bleu.perl
219
LangPair Size Datasources
bn-en 1.70M alt, cvit-pib, jw, opensubtitles, pmi, tanzil, ted2020, wikimatrix
gu-en 0.51M bibleuedin, cvit, jw, pmi, ted2020, urst, wikititles
hi-en 3.50M alt, bibleuedin, cvit-pib, iitb, jw, opensubtitles, pmi, tanzil, ted2020, wikimatrix
kn-en 0.39M bibleuedin, jw, pmi, ted2020
ml-en 1.20M bibleudein, cvit-pib, jw, opensubtitles, pmi, tanzil, ted2020, wikimatrix
mr-en 0.78M bibleuedin, cvit-pib, jw, pmi, ted2020, wikimatrix
or-en 0.25M cvit, mtenglish2odia, odiencorp, pmi
pa-en 0.51M cvit-pib, jw, pmi, ted2020
ta-en 1.40M cvit-pib, jw, nlpc, opensubtitles, pmi, tanzil, ted2020, ufal, wikimatrix, wikititles
te-en 0.68M cvit-pib, jw, opensubtitles, pmi, ted2020, wikimatrix
Table 1: Parallel Dataset amongst 10 Indic-English language pairs. Size is the number of parallel sentences (in
millions). (bn, gu, hi, kn, ml, mr, or, pa, ta, te and en: Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi,
Oriya, Punjabi, Tamil, Telugu and English respectively
BLEU AMFM
LangPair Bilingual IA-En DR-En All-En IA-En DR-En All-En
bn-en 18.52 20.18 - 18.48 0.734491 - 0.730379
gu-en 26.51 31.02 - 28.79 0.776935 - 0.765441
hi-en 33.53 33.7 - 30.9 0.791408 - 0.775032
mr-en 21.28 25.5 - 23.57 0.767347 - 0.751917
or-en 22.6 26.34 - 25.05 0.780009 - 0.770941
pa-en 29.92 32.34 - 29.87 0.782112 - 0.772655
kn-en 17.93 - 24.18 24.01 - 0.744802 0.751223
ml-en 19.52 - 22.84 22.1 - 0.745908 0.744459
ta-en 23.62 - 22.75 21.37 - 0.74509 0.742311
te-en 19.89 - 24.02 22.37 - 0.745885 0.743435
Table 2: Results: XX-en is the translation direction. IA, DR, All are Indo-Aryan, Dravidian and All Indic lan-
guages respectively. The numbers under BLEU and AMFM headings represent BLEU score and AMFM score
respectively.
the evaluation of MultiIndicMT task6. Tokeniza- The BLEU score in table 2 highlights that the
tion is performed using moses-tokenizer (Koehn multilingual model outperforms the simpler bilin-
et al., 2007). For IA-En, DR-En, and All-En, we re- gual models. Although we did not submit bilingual
port results provided by the organizers. Table 2 also models in the shared task submission, we use it
reports the Adequacy-Fluency Metrics (AM-FM) here as a baseline to compare with multilingual
for Machine Translation (MT) Evaluation (Banchs models. Moreover, upon grouping languages based
et al., 2015) provided by organizers. ontheirlanguagefamilies,significantimprovement
in BLEUscores is observed due to less confusion
6http://lotus.kuee.kyoto-u.ac.jp/WAT/ and better learning of the language representations
evaluation/automatic_evaluation_systems/ in shared encoder-decoder architecture. We ob-
automaticEvaluationEN.html
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