1 code implementation • NAACL 2021 • Sergey Volokhin, Joyce Ho, Oleg Rokhlenko, Eugene Agichtein
We call our proposed method ConvExtr (Conversational Collaborative Filtering using External Data), which 1) infers a user{'}s sentiment towards an entity from the conversation context, and 2) transforms the ratings of {``}similar{''} external reviewers to predict the current user{'}s preferences.
no code implementations • NAACL 2021 • Tao Meng, Anjie Fang, Oleg Rokhlenko, Shervin Malmasi
We propose GEMNET, a novel approach for gazetteer knowledge integration, including (1) a flexible Contextual Gazetteer Representation (CGR) encoder that can be fused with any word-level model; and (2) a Mixture-of- Experts gating network that overcomes the feature overuse issue by learning to conditionally combine the context and gazetteer features, instead of assigning them fixed weights.
no code implementations • EACL 2021 • Simone Filice, Giuseppe Castellucci, Marcus Collins, Eugene Agichtein, Oleg Rokhlenko
This common user intent is usually available through a {``}filter-by{''} interface on online shopping websites, but is challenging to support naturally via voice, as the intent of refinements must be interpreted in the context of the original search, the initial results, and the available product catalogue facets.
no code implementations • WS 2018 • Nut Limsopatham, Oleg Rokhlenko, David Carmel
Recent advances in automatic speech recognition lead toward enabling a voice conversation between a human user and an intelligent virtual assistant.
no code implementations • 7 Dec 2017 • Li Zhou, Kevin Small, Oleg Rokhlenko, Charles Elkan
Learning a goal-oriented dialog policy is generally performed offline with supervised learning algorithms or online with reinforcement learning (RL).
no code implementations • 10 Jun 2014 • Oren Anava, Shahar Golan, Nadav Golbandi, Zohar Karnin, Ronny Lempel, Oleg Rokhlenko, Oren Somekh
It is well known that collaborative filtering (CF) based recommender systems provide better modeling of users and items associated with considerable rating history.