activity
20162025
most citedRewriting Conversational Utterances with Instructed Large Language Models

4 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.IR2025★ 1 cited

Efficient Conversational Search via Topical Locality in Dense Retrieval

Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego +2

Pre-trained language models have been widely exploited to learn dense representations of documents and queries for information retrieval. While previous efforts have primarily focu…

cs.CL2024★ 4 cited

Rewriting Conversational Utterances with Instructed Large Language Models

Elnara Galimzhanova, Cristina Ioana Muntean, Franco Maria Nardini +2

Many recent studies have shown the ability of large language models (LLMs) to achieve state-of-the-art performance on many NLP tasks, such as question answering, text summarization…

cs.IR2022

Caching Historical Embeddings in Conversational Search

Ophir Frieder, Ida Mele, Cristina Ioana Muntean +3

Rapid response, namely low latency, is fundamental in search applications; it is particularly so in interactive search sessions, such as those encountered in conversational setting…

cs.IR2021

MICROS: Mixed-Initiative ConveRsatiOnal Systems Workshop

Ida Mele, Cristina Ioana Muntean, Mohammad Aliannejadi +1

The 1st edition of the workshop on Mixed-Initiative ConveRsatiOnal Systems (MICROS@ECIR2021) aims at investigating and collecting novel ideas and contributions in the field of conv…

cs.IR2020

Topic Propagation in Conversational Search

I. Mele, C. I. Muntean, F. M. Nardini +3

In a conversational context, a user expresses her multi-faceted information need as a sequence of natural-language questions, i.e., utterances. Starting from a given topic, the con…

cs.SI2016

Sentiment-enhanced Multidimensional Analysis of Online Social Networks: Perception of the Mediterranean Refugees Crisis

Mauro Coletto, Claudio Lucchese, Cristina Ioana Muntean +4

We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis…