Search Results for author: Oleg Rokhlenko

Found 22 papers, 2 papers with code

SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER)

no code implementations SemEval (NAACL) 2022 Shervin Malmasi, Anjie Fang, Besnik Fetahu, Sudipta Kar, Oleg Rokhlenko

Divided into 13 tracks, the task focused on methods to identify complex named entities (like names of movies, products and groups) in 11 languages in both monolingual and multi-lingual scenarios.

named-entity-recognition Named Entity Recognition +1

Evaluation Metrics of Language Generation Models for Synthetic Traffic Generation Tasks

no code implementations21 Nov 2023 Simone Filice, Jason Ingyu Choi, Giuseppe Castellucci, Eugene Agichtein, Oleg Rokhlenko

Experiments on three tasks, i. e., Shopping Utterance Generation, Product Question Generation and Query Auto Completion, demonstrate that our metrics are effective for evaluating STG tasks, and improve the agreement with human judgement up to 20% with respect to common NLG metrics.

Question Generation Question-Generation +1

Follow-on Question Suggestion via Voice Hints for Voice Assistants

no code implementations25 Oct 2023 Besnik Fetahu, Pedro Faustini, Giuseppe Castellucci, Anjie Fang, Oleg Rokhlenko, Shervin Malmasi

Using a new dataset of 6681 input questions and human written hints, we evaluated the models with automatic metrics and human evaluation.

MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition

no code implementations20 Oct 2023 Besnik Fetahu, Zhiyu Chen, Sudipta Kar, Oleg Rokhlenko, Shervin Malmasi

We present MULTICONER V2, a dataset for fine-grained Named Entity Recognition covering 33 entity classes across 12 languages, in both monolingual and multilingual settings.

named-entity-recognition Named Entity Recognition +2

Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search

no code implementations6 Jun 2023 Zhiyu Chen, Jason Choi, Besnik Fetahu, Oleg Rokhlenko, Shervin Malmasi

We propose an intent-aware FAQ retrieval system consisting of (1) an intent classifier that predicts when a user's information need can be answered by an FAQ; (2) a reformulation model that rewrites a query into a natural question.


Faithful Low-Resource Data-to-Text Generation through Cycle Training

1 code implementation24 May 2023 Zhuoer Wang, Marcus Collins, Nikhita Vedula, Simone Filice, Shervin Malmasi, Oleg Rokhlenko

Cycle training uses two models which are inverses of each other: one that generates text from structured data, and one which generates the structured data from natural language text.

Data-to-Text Generation

Preventing Catastrophic Forgetting in Continual Learning of New Natural Language Tasks

no code implementations22 Feb 2023 Sudipta Kar, Giuseppe Castellucci, Simone Filice, Shervin Malmasi, Oleg Rokhlenko

In this paper, we approach the problem of incrementally expanding MTL models' capability to solve new tasks over time by distilling the knowledge of an already trained model on n tasks into a new one for solving n+1 tasks.

Continual Learning Multi-Task Learning

Reinforced Question Rewriting for Conversational Question Answering

no code implementations27 Oct 2022 Zhiyu Chen, Jie Zhao, Anjie Fang, Besnik Fetahu, Oleg Rokhlenko, Shervin Malmasi

Furthermore, human evaluation shows that our method can generate more accurate and detailed rewrites when compared to human annotations.

Question Rewriting Retrieval

MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition

no code implementations COLING 2022 Shervin Malmasi, Anjie Fang, Besnik Fetahu, Sudipta Kar, Oleg Rokhlenko

We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets.

Machine Translation named-entity-recognition +2

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 +1

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 Named Entity Recognition +1

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

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

Cannot find the paper you are looking for? You can Submit a new open access paper.