Noisy Text Data: Achilles’ Heel of BERT
Pre-trained language models such as BERT have performed very well for various NLP tasks like text classification, question answering etc. Given BERT success, industry practitioners are actively experimenting with fine-tuning BERT to build NLP applications for solving their use cases like search recommendation, sentiment analysis, opinion mining etc. As compared to the benchmark datasets, datasets used to build industrial NLP applications are often much more noisy. While BERT has performed exceedingly well for transferring the learnings from one use case to another, it remains unclear how BERT performs when fine tuned on non-canonical text.
In this talk, we systematically dig deeper into BERT architecture and show the effect of noisy text data on final outcome. We systematically show that when the text data is noisy (spelling mistakes, typos), there is a significant degradation in the performance of BERT. We further analyze the reasons and shortcomings in the existing BERT pipeline that are responsible for this drop in performance.
This work is motivated from the business use case where we are building a dialogue system over WhatsApp to screen candidates for blue collar jobs. Our candidate user base often comes from underprivileged backgrounds, hence most of them are unable to complete college graduation. This coupled with fat finger problem over a mobile keypad leads to a lot of typos and spelling mistakes in the responses received by our dialogue system.
While this work is motivated from our business use case, our findings are applicable across various use cases in industry that deal with non-canonical text - from sentiment classification on twitter data to entity extraction over text collected from discussion forums.
Outline/Structure of the Talk
- Introduction (1~2 mins)
- Understanding characteristics of noisy text data (2 mins)
- Review of BERT architecture(2 mins)
- Problem statement (3 mins)
- Experimentation setup(3 mins)
- Discussion about effect of noise on pre-trained models like BERT(4 mins)
- Effect of different tokenizers (2 mins)
- Tips for using BERT on UGC(2 mins)
- Questions from audience
- Understanding characteristics of user generated data
- Effect of noise on pre-trained language models like BERT
- Effect of noise intext data over various tokenization techniques
- Understanding scenarios where BERT is likely to perform badly.
Data Scientist,NLP engineer, NLP researcher
Prerequisites for Attendees
Basic understanding of NLP
schedule Submitted 2 years ago
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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.
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