12 citations · 61 across the 25 of their papers we have counts for
32 papers
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…
Beyond Relevant Documents: A Knowledge-Intensive Approach for Query-Focused Summarization using Large Language Models
Weijia Zhang, Jia-Hong Huang, Svitlana Vakulenko +3
Query-focused summarization (QFS) is a fundamental task in natural language processing with broad applications, including search engines and report generation. However, traditional…
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…
Focusing on Context is NICE: Improving Overshadowed Entity Disambiguation
Vera Provatorova, Simone Tedeschi, Svitlana Vakulenko +2
Entity disambiguation (ED) is the task of mapping an ambiguous entity mention to the corresponding entry in a structured knowledge base. Previous research showed that entity oversh…
On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question Answering
Georgios Sidiropoulos, Svitlana Vakulenko, Evangelos Kanoulas
Interacting with a speech interface to query a Question Answering (QA) system is becoming increasingly popular. Typically, QA systems rely on passage retrieval to select candidate…
Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey
Xiaoyu Shen, Svitlana Vakulenko, Marco del Tredici +3
Dense retrieval (DR) approaches based on powerful pre-trained language models (PLMs) achieved significant advances and have become a key component for modern open-domain question-a…
A Simple Contrastive Learning Objective for Alleviating Neural Text Degeneration
Shaojie Jiang, Ruqing Zhang, Svitlana Vakulenko +1
The cross-entropy objective has proved to be an all-purpose training objective for autoregressive language models (LMs). However, without considering the penalization of problemati…
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,…
Tackling Query-Focused Summarization as A Knowledge-Intensive Task: A Pilot Study
Weijia Zhang, Svitlana Vakulenko, Thilina Rajapakse +2
Query-focused summarization (QFS) requires generating a summary given a query using a set of relevant documents. However, such relevant documents should be annotated manually and t…
VerbCL: A Dataset of Verbatim Quotes for Highlight Extraction in Case Law
Julien Rossi, Svitlana Vakulenko, Evangelos Kanoulas
Citing legal opinions is a key part of legal argumentation, an expert task that requires retrieval, extraction and summarization of information from court decisions. The identifica…
Robustness Evaluation of Entity Disambiguation Using Prior Probes:the Case of Entity Overshadowing
Vera Provatorova, Svitlana Vakulenko, Samarth Bhargav +1
Entity disambiguation (ED) is the last step of entity linking (EL), when candidate entities are reranked according to the context they appear in. All datasets for training and eval…
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…
A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering
Svitlana Vakulenko, Shayne Longpre, Zhucheng Tu +1
The dependency between an adequate question formulation and correct answer selection is a very intriguing but still underexplored area. In this paper, we show that question rewriti…
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…