Search Results for author:
Found 6 papers, 1 papers with code
Learning to Represent Human Motives for Goal-directed Web Browsing
Then we incorporate the resulting representations for enhancing the experiences of common activities people perform on the web.
Long Document Ranking with Query-Directed Sparse Transformer
In this paper, we design Query-Directed Sparse attention that induces IR-axiomatic structures in transformer self-attention.
Incorporating Behavioral Hypotheses for Query Generation
Generative neural networks have been shown effective on query suggestion.
Characterizing Reading Time on Enterprise Emails
Our approach starts with a reading time analysis based on the reading events from a major email platform, followed by a user study to provide explanations for some discoveries.
Learning Word Representations with Regularization from Prior Knowledge
This paper presents a unified framework that leverages pre-learned or external priors, in the form of a regularizer, for enhancing conventional language model-based embedding learning.
Learning User Embeddings from Emails
Many important email-related tasks, such as email classification or search, highly rely on building quality document representations (e. g., bag-of-words or key phrases) to assist matching and understanding.
