Papers by Rakesh Chada
Error Detection in Large-Scale Natural Language Understanding Systems Using Transformer Models (2021.findings-acl)
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| Challenge: | Large-scale conversational assistants process every utterance using multiple models for domain, intent and named entity recognition. |
| Approach: | They combine utterance encodings from a RoBERTa model with the Nbest hypothesis produced by the production system to detect domain classification errors. |
| Outcome: | The proposed approach outperforms bi-LSTM models and a standalone model by 2.2% to 32.2% by ensembling multiple models. |
FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models (2021.emnlp-main)
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| Challenge: | Existing pre-trained models need fine-tuning on tens of thousands of examples to achieve good results. |
| Approach: | They propose a framework that leverages pre-trained text-to-text models and aligns them with their pre-training framework. |
| Outcome: | The proposed framework outperforms the XLM-Roberta-large on multiple QA benchmarks and is applicable to multilingual situations. |
FPI: Failure Point Isolation in Large-scale Conversational Assistants (2022.naacl-industry)
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| Challenge: | Large-scale conversational assistants can cause errors in their modules . a machine learning system can analyze large volumes of data and isolate the source of error . |
| Approach: | They propose a machine learning system that embeds incoming request and context using pre-trained transformer models and encodes additional metadata features to output failure point predictions. |
| Outcome: | The proposed system obtains 92.2% of human performance while scaling to analyze the entire traffic in 8 different languages of a large-scale conversational assistant. |
CGF: Constrained Generation Framework for Query Rewriting in Conversational AI (2022.emnlp-industry)
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Jie Hao, Yang Liu, Xing Fan, Saurabh Gupta, Saleh Soltan, Rakesh Chada, Pradeep Natarajan, Chenlei Guo, Gokhan Tur
| Challenge: | Large-scale conversational AI agents such as Alexa, Siri and Google Assistant help millions of users to perform a lot of tasks. |
| Approach: | They propose a Constrained Generation Framework for query rewriting at global and personalized levels. |
| Outcome: | The proposed framework significantly boosts the query rewriting performance. |