20 citations · 33 across the 7 of their papers we have counts for
7 papers
ImPaKT: A Dataset for Open-Schema Knowledge Base Construction
Luke Vilnis, Zach Fisher, Bhargav Kanagal +2
Large language models have ushered in a golden age of semantic parsing. The seq2seq paradigm allows for open-schema and abstractive attribute and relation extraction given only sma…
MAVE: A Product Dataset for Multi-source Attribute Value Extraction
Li Yang, Qifan Wang, Zac Yu +5
Attribute value extraction refers to the task of identifying values of an attribute of interest from product information. Product attribute values are essential in many e-commerce…
ShopTalk: A System for Conversational Faceted Search
Gurmeet Manku, James Lee-Thorp, Bhargav Kanagal +11
We present ShopTalk, a multi-turn conversational faceted search system for shopping that is designed to handle large and complex schemas that are beyond the scope of state of the a…
DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections
Yury Zemlyanskiy, Sudeep Gandhe, Ruining He +5
This paper explores learning rich self-supervised entity representations from large amounts of the associated text. Once pre-trained, these models become applicable to multiple ent…
RealFormer: Transformer Likes Residual Attention
Ruining He, Anirudh Ravula, Bhargav Kanagal +1
Transformer is the backbone of modern NLP models. In this paper, we propose RealFormer, a simple and generic technique to create Residual Attention Layer Transformer networks that…
A Generic Coordinate Descent Framework for Learning from Implicit Feedback
Immanuel Bayer, Xiangnan He, Bhargav Kanagal +1
In recent years, interest in recommender research has shifted from explicit feedback towards implicit feedback data. A diversity of complex models has been proposed for a wide vari…
Supercharging Recommender Systems using Taxonomies for Learning User Purchase Behavior
Bhargav Kanagal, Amr Ahmed, Sandeep Pandey +3
Recommender systems based on latent factor models have been effectively used for understanding user interests and predicting future actions. Such models work by projecting the user…