83 citations · 225 across the 26 of their papers we have counts for
20 papers · 1 filter
Customer-Agent: Overcoming Context Limitations in Ultra-Long Shopping Trajectories via Tool-Augmented Agents and RLVR
Hongye Liu, Rongmei Lin, Anurag Kashyap +4
Understanding customer shopping trajectories is essential for enabling personalized shopping experiences. However, shopping records (i.e., customer's search, clicks, purchases, etc…
ByteFlow: Language Modeling through Adaptive Byte Compression without a Tokenizer
Chunyuan Deng, Sanket Lokegaonkar, Colin Lockard +3
Modern language models still rely on fixed, pre-defined subword tokenizations. Once a tokenizer is trained, the LM can only operate at this fixed level of granularity, which often…
Stepwise Penalization for Length-Efficient Chain-of-Thought Reasoning
Xintong Li, Sha Li, Rongmei Lin +10
Large reasoning models improve with more test-time computation, but often overthink, producing unnecessarily long chains-of-thought that raise cost without improving accuracy. Prio…
Wizard of Shopping: Target-Oriented E-commerce Dialogue Generation with Decision Tree Branching
Xiangci Li, Zhiyu Chen, Jason Ingyu Choi +4
The goal of conversational product search (CPS) is to develop an intelligent, chat-based shopping assistant that can directly interact with customers to understand shopping intents…
Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers
Lütfi Kerem Senel, Besnik Fetahu, Davis Yoshida +5
Recommender systems are widely used to suggest engaging content, and Large Language Models (LLMs) have given rise to generative recommenders. Such systems can directly generate ite…
Identifying Shopping Intent in Product QA for Proactive Recommendations
Besnik Fetahu, Nachshon Cohen, Elad Haramaty +3
Voice assistants have become ubiquitous in smart devices allowing users to instantly access information via voice questions. While extensive research has been conducted in question…
Instant Answering in E-Commerce Buyer-Seller Messaging using Message-to-Question Reformulation
Besnik Fetahu, Tejas Mehta, Qun Song +3
E-commerce customers frequently seek detailed product information for purchase decisions, commonly contacting sellers directly with extended queries. This manual response requireme…
Controllable Decontextualization of Yes/No Question and Answers into Factual Statements
Lingbo Mo, Besnik Fetahu, Oleg Rokhlenko +1
Yes/No or polar questions represent one of the main linguistic question categories. They consist of a main interrogative clause, for which the answer is binary (assertion or negati…
Follow-on Question Suggestion via Voice Hints for Voice Assistants
Besnik Fetahu, Pedro Faustini, Giuseppe Castellucci +3
The adoption of voice assistants like Alexa or Siri has grown rapidly, allowing users to instantly access information via voice search. Query suggestion is a standard feature of sc…
InstructPTS: Instruction-Tuning LLMs for Product Title Summarization
Besnik Fetahu, Zhiyu Chen, Oleg Rokhlenko +1
E-commerce product catalogs contain billions of items. Most products have lengthy titles, as sellers pack them with product attributes to improve retrieval, and highlight key produ…
MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition
Besnik Fetahu, Zhiyu Chen, Sudipta Kar +2
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. This da…
Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search
Zhiyu Chen, Jason Choi, Besnik Fetahu +2
Customers interacting with product search engines are increasingly formulating information-seeking queries. Frequently Asked Question (FAQ) retrieval aims to retrieve common questi…
Answering Unanswered Questions through Semantic Reformulations in Spoken QA
Pedro Faustini, Zhiyu Chen, Besnik Fetahu +2
Spoken Question Answering (QA) is a key feature of voice assistants, usually backed by multiple QA systems. Users ask questions via spontaneous speech which can contain disfluencie…
SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)
Besnik Fetahu, Sudipta Kar, Zhiyu Chen +2
We present the findings of SemEval-2023 Task 2 on Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2). Divided into 13 tracks, the task focused on methods to identify…
Reinforced Question Rewriting for Conversational Question Answering
Zhiyu Chen, Jie Zhao, Anjie Fang +3
Conversational Question Answering (CQA) aims to answer questions contained within dialogues, which are not easily interpretable without context. Developing a model to rewrite conve…
MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition
Shervin Malmasi, Anjie Fang, Besnik Fetahu +2
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…
Neural OCR Post-Hoc Correction of Historical Corpora
Lijun Lyu, Maria Koutraki, Martin Krickl +1
Optical character recognition (OCR) is crucial for a deeper access to historical collections. OCR needs to account for orthographic variations, typefaces, or language evolution (i.…
Neural Based Statement Classification for Biased Language
Christoph Hube, Besnik Fetahu
Biased language commonly occurs around topics which are of controversial nature, thus, stirring disagreement between the different involved parties of a discussion. This is due to…
Approaches for Enriching and Improving Textual Knowledge Bases
Besnik Fetahu
Verifiability is one of the core editing principles in Wikipedia, where editors are encouraged to provide citations for the added statements. Statements can be any arbitrary piece…
Fine Grained Citation Span for References in Wikipedia
Besnik Fetahu, Katja Markert, Avishek Anand
\emph{Verifiability} is one of the core editing principles in Wikipedia, editors being encouraged to provide citations for the added content. For a Wikipedia article, determining t…