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Jason D. Williams

4 accepted papers

2021

Generating Natural Questions from Images for Multimodal Assistants

ICASSP 2021accepted

Generating natural, diverse, and meaningful questions from images is an essential task for multimodal assistants as it confirms whether they have understood the object and scene in the images properly. The research in visual question answering (VQA) and visual question generation (VQG) is a great st…

Cited by 0SourceScholar
2021

Noise Robust Named Entity Understanding for Voice Assistants

NAACL 2021industry

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…

Cited by 5SourcePDFScholar
2020

Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution

COLING 2020main

This paper develops and implements a scalable methodology for (a) estimating the noisiness of labels produced by a typical crowdsourcing semantic annotation task, and (b) reducing the resulting error of the labeling process by as much as 20-30% in comparison to other common labeling strategies. Impo…

Cited by 14SourcePDFScholar
2020

Lattice-Based Improvements for Voice Triggering Using Graph Neural Networks

ICASSP 2020accepted

Voice-triggered smart assistants often rely on detection of a trigger-phrase before they start listening for the user request. Mitigation of false triggers is an important aspect of building a privacy-centric non-intrusive smart assistant. In this paper, we address the task of false trigger mitigati…

Cited by 0SourceScholar