and language technologies. This will help pool low resources across various languages to build robust ASR systems for Indian languages.

˲ In the context of TTS, the major issue to be addressed is the input method. Text is available in multiple Indian scripts, but digital resources in terms of high-quality parallel corpora are few and far between. In the context of both ASR and TTS, generic acoustic models across various languages, generic language models in the former, and a generic Indic voice in the latter need to be designed. This will also address the issue of code switching.

˲ In T TS, code mixing must find ways to preserve the speaker’s voice across languages. Further, the influence of the native tongue on a non-native tongue must be preserved. For instance, there are as many varieties of English as there are native tongues. Replacing non-native English (which is syllable-timed) with stress-timed English can make it difficult for the listener to understand.

˲ Text in social media generally includes code switching/mixing. Further, there are many words that have a local cultural connotation. Building language resources to address these requires the expertise of linguists, speech scientists, natural language processing engineers, and ethnographers.

˲ Data is the new oil, and NLP and ILT is no exception. There is no doubt that resources with quality and coverage need to be created, and created fast.

Thinking creatively on how to engage even a small portion of 1 billion hands for resource creation is a must. Crowdsourcing, in spite of its criticism with respect to quality, seems to be the way forward. Providing attractive, helpful interfaces and remuneration can go a long way toward resource creation. In this context, the Language Data Consortium for Indian Languages (LDC-IL)p initiative of Central Institute of Indian Languages (CIIL) is noteworthy.

˲ Evaluation is the key to actual use of
language resources and should be taken
very seriously. Like TRECq (USA), CLEFr
(Europe), and NTCIRs (CJK countries),
India’s Forum for Information Retrieval
p http://www.ldcil.org/
q https://trec.nist.gov/
r http://www.clef-initiative.eu/
s http://research.nii.ac.jp/ntcir/index-en.html
Evaluation (FIRE) initiativet has taken up
the cause of evaluation in information
retrieval and allied tasks. A FIRE-like
initiative is needed for all areas of ILT.

Conclusion

Indic Language Computing (ILC) is too important a problem to be lying in oblivion. Given spectacular advancements to date in computing science and technology, Internet, AI, machine learning, and NLP, the time is ripe for a concerted thrust for realization and social penetration of ILC. The energy of the start-up echo system has to be harnessed with government support, and guidance from academia. Language resource creation is a precondition for ILC revolution, and as in all cases of large infrastructure building (roads, internet, gas lines, waterways), government spon-sorship is needed for resource building.

t http://fire.irsi.res.in/fire/2019/home

References

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ICLR, 2015.

2. Bhattacharyya, P. Natural language processing: A perspective from computation in presence of ambiguity, resource constraint and multilinguality. CSI J. Computer Science and Engineering 1, 2 (2012).

3. Chakrabarti, D., Mandalia, H., Priya, R., Sarma, V., and Bhattacharyya, P. Hindi compound verbs and their automatic extraction. In Proceedings of Computational Linguistics, Manchester, U.K., Aug. 2008.

4. Jha, G. N. The TDIL program and the Indian language corpora initiative. In Proceedings of LREC, 2010.

5. Koehn, P. Europarl: A parallel corpus for statistical machine translation. In Proceedings of the Machine Translation Summit, 2005.

6. Kunchukuttan, A., Mishra, A., Chatterjee, R., Shah, R. and Bhattacharyya, P. Shata-Anuvadak: Tackling multiway translation of Indian languages. In Proceedings of the Language Resources Evaluation Conference, 2014.

7. Kunchukuttan, A., Mehta, P., and Bhattacharyya, P.

The IIT Bombay English-Hindi parallel corpus. In Proceedings of LREC, (Miyazaki, Japan, May 7–12, 2018).

8. Liu. B. Sentiment Analysis and Opinion Mining. Morgan and Claypool Publishers, 2012.

9. Murthy, R., Kunchukuttan, A., and Bhattacharyya, P.

Addressing word-order divergence in multilingual neural machine translation for extremely low resource languages. In Proceedings of LREC, 2019.

10. Ranathunga, S., Farhath, F., Thayasivam, U., Jayasena, S., and Dias, G. Si-Ta: Machine translation of Sinhala and Tamil official documents. In Proceedings of the National Information Technology Conference, 2019.

11. Subbarao K. V. South Asian Languages—A Syntactic Typology. Cambridge, 2012.

 

Pushpak Bhattacharyya ( [e-mail]) is a professor in the computer science and engineering department of IIT Bombay, and director of IIT Patna.

Hema Murthy ( [e-mail]) is a professor in the computer science and engineering department of IIT Madras.

Surangika Ranathunga ( [e-mail]) is a senior lecturer in the department of computer science and engineering and a member of the faculty of engineering at the University of Moratuwa.

Ranjiva Munasinghe ( [e-mail]) is chief executive officer of MIND Analytics and Management in Colombo, Sri Lanka.

© 2019 ACM 0001-0782/19/11 $15.00

Code mixing
must find ways
to preserve
the speaker’s voice
across languages.
Further,
the influence
of the native tongue
on a non-native
tongue must
be preserved.