12 citations · 61 across the 25 of their papers we have counts for
20 papers · 1 filter
Understanding and Debugging Failures in N-Gram-Based Generative Retrieval
Richard Takacs, Adrian Bracher, Svitlana Vakulenko
Generative Retrieval (GR) is an emerging Information Retrieval (IR) paradigm that is motivated by increasingly capable language models. In GR, a model directly generates identifier…
Generative Retrieval Overcomes Limitations of Dense Retrieval but Struggles with Identifier Ambiguity
Adrian Bracher, Svitlana Vakulenko
While dense retrieval models, which embed queries and documents into a shared low-dimensional space, have gained widespread popularity, they were shown to exhibit important theoret…
Orcheo: A Modular Full-Stack Platform for Conversational Search
Shaojie Jiang, Svitlana Vakulenko, Maarten de Rijke
Conversational search (CS) requires a complex software engineering pipeline that integrates query reformulation, ranking, and response generation. CS researchers currently face two…
Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study
Mariya Hendriksen, Svitlana Vakulenko, Ernst Kuiper +1
Most approaches to cross-modal retrieval (CMR) focus either on object-centric datasets, meaning that each document depicts or describes a single object, or on scene-centric dataset…
Extending CLIP for Category-to-image Retrieval in E-commerce
Mariya Hendriksen, Maurits Bleeker, Svitlana Vakulenko +3
E-commerce provides rich multimodal data that is barely leveraged in practice. One aspect of this data is a category tree that is being used in search and recommendation. However,…
Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering
Georgios Sidiropoulos, Nikos Voskarides, Svitlana Vakulenko +1
In simple open-domain question answering (QA), dense retrieval has become one of the standard approaches for retrieving the relevant passages to infer an answer. Recently, dense re…
A Large-Scale Analysis of Mixed Initiative in Information-Seeking Dialogues for Conversational Search
Svitlana Vakulenko, Evangelos Kanoulas, Maarten de Rijke
Conversational search is a relatively young area of research that aims at automating an information-seeking dialogue. In this paper we help to position it with respect to other res…
Leveraging Query Resolution and Reading Comprehension for Conversational Passage Retrieval
Svitlana Vakulenko, Nikos Voskarides, Zhucheng Tu +1
This paper describes the participation of UvA.ILPS group at the TREC CAsT 2020 track. Our passage retrieval pipeline consists of (i) an initial retrieval module that uses BM25, and…
A Comparison of Question Rewriting Methods for Conversational Passage Retrieval
Svitlana Vakulenko, Nikos Voskarides, Zhucheng Tu +1
Conversational passage retrieval relies on question rewriting to modify the original question so that it no longer depends on the conversation history. Several methods for question…
Conversational Browsing
Svitlana Vakulenko, Vadim Savenkov, Maarten de Rijke
How can we better understand the mechanisms behind multi-turn information seeking dialogues? How can we use these insights to design a dialogue system that does not require explici…
Open-Domain Question Answering Goes Conversational via Question Rewriting
Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu +3
We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs. The task in QReCC is to find a…
An Analysis of Mixed Initiative and Collaboration in Information-Seeking Dialogues
Svitlana Vakulenko, Evangelos Kanoulas, Maarten de Rijke
The ability to engage in mixed-initiative interaction is one of the core requirements for a conversational search system. How to achieve this is poorly understood. We propose a set…
Question Rewriting for Conversational Question Answering
Svitlana Vakulenko, Shayne Longpre, Zhucheng Tu +1
Conversational question answering (QA) requires the ability to correctly interpret a question in the context of previous conversation turns. We address the conversational QA task b…
Common Conversational Community Prototype: Scholarly Conversational Assistant
Krisztian Balog, Lucie Flekova, Matthias Hagen +6
This paper discusses the potential for creating academic resources (tools, data, and evaluation approaches) to support research in conversational search, by focusing on realistic i…
Knowledge-based Conversational Search
Svitlana Vakulenko
Conversational interfaces that allow for intuitive and comprehensive access to digitally stored information remain an ambitious goal. In this thesis, we lay foundations for designi…
Open Data Chatbot
Sophia Keyner, Vadim Savenkov, Svitlana Vakulenko
Recently, chatbots received an increased attention from industry and diverse research communities as a dialogue-based interface providing advanced human-computer interactions. On t…
QRFA: A Data-Driven Model of Information-Seeking Dialogues
Svitlana Vakulenko, Kate Revoredo, Claudio Di Ciccio +1
Understanding the structure of interaction processes helps us to improve information-seeking dialogue systems. Analyzing an interaction process boils down to discovering patterns i…
Talking Open Data
Sebastian Neumaier, Vadim Savenkov, Svitlana Vakulenko
Enticing users into exploring Open Data remains an important challenge for the whole Open Data paradigm. Standard stock interfaces often used by Open Data portals are anything but…
TableQA: Question Answering on Tabular Data
Svitlana Vakulenko, Vadim Savenkov
Tabular data is difficult to analyze and to search through, yielding for new tools and interfaces that would allow even non tech-savvy users to gain insights from open datasets wit…
Character-based Neural Embeddings for Tweet Clustering
Svitlana Vakulenko, Lyndon Nixon, Mihai Lupu
In this paper we show how the performance of tweet clustering can be improved by leveraging character-based neural networks. The proposed approach overcomes the limitations related…