Alkesh Patel


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MARRS: Multimodal Reference Resolution System
Halim Cagri Ates | Shruti Bhargava | Site Li | Jiarui Lu | Siddhardha Maddula | Joel Ruben Antony Moniz | Anil Kumar Nalamalapu | Roman Hoang Nguyen | Melis Ozyildirim | Alkesh Patel | Dhivya Piraviperumal | Vincent Renkens | Ankit Samal | Thy Tran | Bo-Hsiang Tseng | Hong Yu | Yuan Zhang | Shirley Zou
Proceedings of The Sixth Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2023)

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Referring to Screen Texts with Voice Assistants
Shruti Bhargava | Anand Dhoot | Ing-marie Jonsson | Hoang Long Nguyen | Alkesh Patel | Hong Yu | Vincent Renkens
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)

Voice assistants help users make phone calls, send messages, create events, navigate and do a lot more. However assistants have limited capacity to understand their users’ context. In this work, we aim to take a step in this direction. Our work dives into a new experience for users to refer to phone numbers, addresses, email addresses, urls, and dates on their phone screens. We focus on reference understanding, which is particularly interesting when, similar to visual grounding, there are multiple similar texts on screen. We collect a dataset and propose a lightweight general purpose model for this novel experience. Since consuming pixels directly is expensive, our system is designed to rely only on text extracted from the UI. Our model is modular, offering flexibility, better interpretability and efficient run time memory.


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Noise Robust Named Entity Understanding for Voice Assistants
Deepak Muralidharan | Joel Ruben Antony Moniz | Sida Gao | Xiao Yang | Justine Kao | Stephen Pulman | Atish Kothari | Ray Shen | Yinying Pan | Vivek Kaul | Mubarak Seyed Ibrahim | Gang Xiang | Nan Dun | Yidan Zhou | Andy O | Yuan Zhang | Pooja Chitkara | Xuan Wang | Alkesh Patel | Kushal Tayal | Roger Zheng | Peter Grasch | Jason D Williams | Lin Li
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that jointly solves the NER and EL tasks by combining them in a joint reranking module. We show that our proposed framework improves NER accuracy by up to 3.13% and EL accuracy by up to 3.6% in F1 score. The features used also lead to better accuracies in other natural language understanding tasks, such as domain classification and semantic parsing.