Search Results for author: Oleg Rokhlenko

Found 7 papers, 1 papers with code

You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions

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.

Collaborative Filtering Domain Adaptation

GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input

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.

Named Entity Recognition NER

VoiSeR: A New Benchmark for Voice-Based Search Refinement

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.

Conversational Search

Research Challenges in Building a Voice-based Artificial Personal Shopper - Position Paper

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.

Automatic Speech Recognition Chatbot +2

End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient

no code implementations7 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).

Goal-Oriented Dialog Offline RL +1

Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design

no code implementations10 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.

Collaborative Filtering Recommendation Systems

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