Vincent Renkens


2023

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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.

2018

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State Gradients for RNN Memory Analysis
Lyan Verwimp | Hugo Van hamme | Vincent Renkens | Patrick Wambacq
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

We present a framework for analyzing what the state in RNNs remembers from its input embeddings. We compute the gradients of the states with respect to the input embeddings and decompose the gradient matrix with Singular Value Decomposition to analyze which directions in the embedding space are best transferred to the hidden state space, characterized by the largest singular values. We apply our approach to LSTM language models and investigate to what extent and for how long certain classes of words are remembered on average for a certain corpus. Additionally, the extent to which a specific property or relationship is remembered by the RNN can be tracked by comparing a vector characterizing that property with the direction(s) in embedding space that are best preserved in hidden state space.