Normalizing User-Generated Text Data


A large fraction of work in NLP work in academia and research groups deals with clean datasets that are much more structured and free of noise. However, when it comes to building real-world NLP applications, one often has to collect data from applications such as chats, user-discussion forums, social-media conversations, etc. Invariably all NLP applications in industrial settings that have to deal with much more noisy and varying data - data with spelling mistakes, typos, acronyms, emojis, embedded metadata, etc.
There is a high level of disparity between the data SOTA language models were trained on & the data these models are expected to work on in practice. This renders most commercial NLP applications working with noisy data unable to take advantage of SOTA advances in the field of language computation.
Handcrafting rules and heuristics to correct this data on a large scale might not be a scalable option for most industrial applications. Most SOTA models in NLP are not designed keeping in mind noise in the data. They often give a substandard performance on noisy data.

In this talk, we share our approach, experience, and learnings from designing a robust system to clean noise in data, without handcrafting the rules, using Machine Translation, and effectively making downstream NLP tasks easier to perform.
This work is motivated by our business use case where we are building a conversational system over WhatsApp to screen candidates for blue-collar jobs. Our candidate user base often comes from tier-2 and tier-3 cities of India. Their responses to our conversational bot are mostly a code mix of Hindi and English coupled with non-canonical text (ex: typos, non-standard syntactic constructions, spelling variations, phonetic substitutions, foreign language words in a non-native script, grammatically incorrect text, colloquialisms, abbreviations, etc). The raw text our system gets is far from clean well-formatted text and text normalization becomes a necessity to process it any further.
This talk is meant for computational language researchers/NLP practitioners, ML engineers, data scientists, senior leaders of AI/ML/DS groups & linguists working with non-canonical resource-rich, resource-constrained i.e. vernacular & code-mixed languages.


Piyush is a graduate from Georgia Institute of Technology and is currently working as an NLP Engineer at After passing out from IITR with a Bachelors's in ECE, he started out his career as a 4G protocol engineer but soon got attracted towards the fast-growing ML/AI domain. Over time he switched over to this domain and, after some exploration, found his interest in working with vernacular languages.

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