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20182022
most citedRepresentation based meta-learning for few-shot spoken intent recognition

9 citations · 9 across the 5 of their papers we have counts for

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6 papers · 1 filter

cs.CL2022

Adaptive Discounting of Implicit Language Models in RNN-Transducers

Vinit Unni, Shreya Khare, Ashish Mittal +3

RNN-Transducer (RNN-T) models have become synonymous with streaming end-to-end ASR systems. While they perform competitively on a number of evaluation categories, rare words pose a…

cs.CL2021

Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages

Tejas Indulal Dhamecha, Rudra Murthy, Samarth Bharadwaj +2

We explore the impact of leveraging the relatedness of languages that belong to the same family in NLP models using multilingual fine-tuning. We hypothesize and validate that multi…

cs.CL2021

Topic Transferable Table Question Answering

Saneem Ahmed Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj +5

Weakly-supervised table question-answering(TableQA) models have achieved state-of-art performance by using pre-trained BERT transformer to jointly encoding a question and a table t…

cs.CL20219 cited

Representation based meta-learning for few-shot spoken intent recognition

Ashish Mittal, Samarth Bharadwaj, Shreya Khare +3

Spoken intent detection has become a popular approach to interface with various smart devices with ease. However, such systems are limited to the preset list of intents-terms or co…

cs.CL2021

AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry

Yannis Katsis, Saneem Chemmengath, Vishwajeet Kumar +8

Recent advances in transformers have enabled Table Question Answering (Table QA) systems to achieve high accuracy and SOTA results on open domain datasets like WikiTableQuestions a…

cs.CL2021

Multilingual and code-switching ASR challenges for low resource Indian languages

Anuj Diwan, Rakesh Vaideeswaran, Sanket Shah +19

Recently, there is increasing interest in multilingual automatic speech recognition (ASR) where a speech recognition system caters to multiple low resource languages by taking adva…