Papers by Abhinav Goyal
Building Accurate Low Latency ASR for Streaming Voice Search in E-commerce (2023.acl-industry)
Copied to clipboard
| Challenge: | Recent years have witnessed the popularity of end-to-end ASR models, which have demonstrated higher accuracy compared to traditional pipelines with separate acoustic, pronunciation, and language models. |
| Approach: | They build accurate LSTM, attention and CTC based streaming ASR models for large-scale Hinglish voice search. |
| Outcome: | The proposed model achieves a word error rate (WER) of 3.69% without EOS and 4.78% with EOS, with 1300 ms (46.64%) reduction in latency. |
IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning (2024.acl-long)
Copied to clipboard
| Challenge: | Legal systems worldwide struggle with exponentially growing legal cases in various courts. |
| Approach: | They propose a benchmark for Indian legal text understanding and reasoning task that includes domain-specific tasks that address different aspects of the legal system. |
| Outcome: | The proposed benchmark for Indian legal text understanding and reasoning aims to address the gap between models and the ground truth. |
End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English (2022.emnlp-industry)
Copied to clipboard
| Challenge: | Experimental results show that an E2E S2I model outperforms a conventional pipeline by 27% on the F1 score . multi-component pipelines require large annotated datasets, have higher latency, and have complex deployment. |
| Approach: | They propose an end-to-end (E2E) S2I model that uses pre-trained automatic speech recognition with slight modification and fine-tuning on small annotated datasets. |
| Outcome: | The proposed model outperforms a conventional pipeline on an annotated dataset by 27% on the F1 score. |
ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts (2022.emnlp-main)
Copied to clipboard
Rajdeep Mukherjee, Abhinav Bohra, Akash Banerjee, Soumya Sharma, Manjunath Hegde, Afreen Shaikh, Shivani Shrivastava, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, Pawan Goyal
| Challenge: | ECTSum is a dataset for bullet-point summarization of earnings calls hosted by publicly traded companies. |
| Approach: | They propose a dataset with transcripts of earnings calls and bullet point summaries derived from Reuters articles. |
| Outcome: | The proposed dataset compares transcripts of earnings calls hosted by publicly traded companies with experts-written bullet point summaries derived from Reuters articles . |