Search Results for author: Rakesh Chada

Found 7 papers, 1 papers with code

FPI: Failure Point Isolation in Large-scale Conversational Assistants

no code implementations NAACL (ACL) 2022 Rinat Khaziev, Usman Shahid, Tobias Röding, Rakesh Chada, Emir Kapanci, Pradeep Natarajan

Large-scale conversational assistants such as Cortana, Alexa, Google Assistant and Siri process requests through a series of modules for wake word detection, speech recognition, language understanding and response generation.

Response Generation speech-recognition +1

FashionVLP: Vision Language Transformer for Fashion Retrieval With Feedback

no code implementations CVPR 2022 Sonam Goenka, Zhaoheng Zheng, Ayush Jaiswal, Rakesh Chada, Yue Wu, Varsha Hedau, Pradeep Natarajan

Fashion image retrieval based on a query pair of reference image and natural language feedback is a challenging task that requires models to assess fashion related information from visual and textual modalities simultaneously.

Image Retrieval

FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models

no code implementations EMNLP 2021 Rakesh Chada, Pradeep Natarajan

On the multilingual TydiQA benchmark, our model outperforms the XLM-Roberta-large by an absolute margin of upto 40 F1 points and an average of 33 F1 points in a few-shot setting (<= 64 training examples).

Few-Shot Learning Question Answering

Simultaneous paraphrasing and translation by fine-tuning Transformer models

no code implementations WS 2020 Rakesh Chada

This paper describes the third place submission to the shared task on simultaneous translation and paraphrasing for language education at the 4th workshop on Neural Generation and Translation (WNGT) for ACL 2020.

Translation

Gendered Pronoun Resolution using BERT and an extractive question answering formulation

1 code implementation WS 2019 Rakesh Chada

In this paper, we propose an extractive question answering (QA) formulation of pronoun resolution task that overcomes this limitation and shows much lower gender bias (0. 99) on their dataset.

Coreference Resolution Extractive Question-Answering +3

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