Search Results for author:
Found 4 papers, 0 papers with code
Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out
Next, we expand the time horizon to examine behavior changes and show that as users discover the limitations of the IA's understanding and functional capabilities, they learn to adjust the scope and wording of their requests to increase the likelihood of receiving a helpful response from the IA.
MMIU: Dataset for Visual Intent Understanding in Multimodal Assistants
In multimodal assistant, where vision is also one of the input modalities, the identification of user intent becomes a challenging task as visual input can influence the outcome.
Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution
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.
Generating Natural Questions from Images for Multimodal Assistants
We evaluate our approach using standard evaluation metrics such as BLEU, METEOR, ROUGE, and CIDEr to show the relevance of generated questions with human-provided questions.
