Search Results for author:
Found 52 papers, 25 papers with code
Towards Self-Contained Answers: Entity-Based Answer Rewriting in Conversational Search
One approach expands answers with inline definitions of salient entities, making the answer self-contained.
IAI MovieBot 2.0: An Enhanced Research Platform with Trainable Neural Components and Transparent User Modeling
While interest in conversational recommender systems has been on the rise, operational systems suitable for serving as research platforms for comprehensive studies are currently lacking.
PKG API: A Tool for Personal Knowledge Graph Management
Personal knowledge graphs (PKGs) offer individuals a way to store and consolidate their fragmented personal data in a central place, improving service personalization while maintaining full user control.
Towards Reliable and Factual Response Generation: Detecting Unanswerable Questions in Information-Seeking Conversations
Specifically, our proposed method employs a sentence-level classifier to detect if the answer is present, then aggregates these predictions on the passage level, and eventually across the top-ranked passages to arrive at a final answerability estimate.
Estimating the Usefulness of Clarifying Questions and Answers for Conversational Search
While the body of research directed towards constructing and generating clarifying questions in mixed-initiative conversational search systems is vast, research aimed at processing and comprehending users' answers to such questions is scarce.
Towards Filling the Gap in Conversational Search: From Passage Retrieval to Conversational Response Generation
However, synthesizing the top retrieved passages into a complete, relevant, and concise response is still an open challenge.
Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences
Inspired by recent successes of prompting paradigms for large language models (LLMs), we study their use for making recommendations from both item-based and language-based preferences in comparison to state-of-the-art item-based collaborative filtering (CF) methods.
User Simulation for Evaluating Information Access Systems
Information access systems, such as search engines, recommender systems, and conversational assistants, have become integral to our daily lives as they help us satisfy our information needs.
MG-ShopDial: A Multi-Goal Conversational Dataset for e-Commerce
To address this, we introduce MG-ShopDial: a dataset of conversations mixing different goals in the domain of e-commerce.
An Ecosystem for Personal Knowledge Graphs: A Survey and Research Roadmap
We propose our own definition of a PKG, emphasizing the aspects of (1) data ownership by a single individual and (2) the delivery of personalized services as the primary purpose.
Measuring the Impact of Explanation Bias: A Study of Natural Language Justifications for Recommender Systems
Despite the potential impact of explanations on decision making, there is a lack of research on quantifying their effect on users' choices.
Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset
Users in consumption domains, like music, are often able to more efficiently provide preferences over a set of items (e. g. a playlist or radio) than over single items (e. g. songs).
Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation
This has motivated conversational recommender systems (CRSs), with control provided through natural language feedback.
From Baseline to Top Performer: A Reproducibility Study of Approaches at the TREC 2021 Conversational Assistance Track
While the results can be reproduced within a 19% relative margin with respect to the main evaluation measure, the relative difference between the baseline and the top performing approach shrinks from the reported 18% to 5%.
UserSimCRS: A User Simulation Toolkit for Evaluating Conversational Recommender Systems
We present an extensible user simulation toolkit to facilitate automatic evaluation of conversational recommender systems.
DAGFiNN: A Conversational Conference Assistant
DAGFiNN is a conversational conference assistant that can be made available for a given conference both as a chatbot on the website and as a Furhat robot physically exhibited at the conference venue.
Would You Ask it that Way? Measuring and Improving Question Naturalness for Knowledge Graph Question Answering
The construction of this test collection also sheds light on the challenges of constructing large-scale KGQA datasets with genuinely NL questions.
On Natural Language User Profiles for Transparent and Scrutable Recommendation
Natural interaction with recommendation and personalized search systems has received tremendous attention in recent years.
Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems
User simulation has been a cost-effective technique for evaluating conversational recommender systems.
Soliciting User Preferences in Conversational Recommender Systems via Usage-related Questions
These strategies do not perform well in cases where the user does not have sufficient knowledge of the target domain to answer such questions.
Semantic Answer Type Prediction using BERT: IAI at the ISWC SMART Task 2020
This paper summarizes our participation in the SMART Task of the ISWC 2020 Challenge.
POINTREC: A Test Collection for Narrative-driven Point of Interest Recommendation
These requests are to be resolved from a dataset of POIs, which are collected from a popular online directory, and are further linked to a geographical knowledge base and enriched with relevant web snippets.
On Interpretation and Measurement of Soft Attributes for Recommendation
We address how to robustly interpret natural language refinements (or critiques) in recommender systems.
Semantic Table Retrieval using Keyword and Table Queries
The main novel contribution of this work is a semantic table retrieval framework for matching information needs (keyword or table queries) against tables.
Conversational Entity Linking: Problem Definition and Datasets
Further, we report on the performance of traditional EL systems on our Conversational Entity Linking dataset, ConEL, and present an extension to these methods to better fit the conversational setting.
Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems
The purpose of the task is to increase the evaluation power of user simulations and to make the simulation more human-like.
ArXivDigest: A Living Lab for Personalized Scientific Literature Recommendation
Providing personalized recommendations that are also accompanied by explanations as to why an item is recommended is a research area of growing importance.
Sanitizing Synthetic Training Data Generation for Question Answering over Knowledge Graphs
The quality of the data and the partitioning of the datasets into training, validation and test splits impact the performance of the models trained on this data.
IAI MovieBot: A Conversational Movie Recommender System
Conversational recommender systems support users in accomplishing recommendation-related goals via multi-turn conversations.
Generating Categories for Sets of Entities
Category systems are central components of knowledge bases, as they provide a hierarchical grouping of semantically related concepts and entities.
Evaluating Conversational Recommender Systems via User Simulation
Conversational information access is an emerging research area.
REL: An Entity Linker Standing on the Shoulders of Giants
Entity linking is a standard component in modern retrieval system that is often performed by third-party toolkits.
Ranked #3 on
Entity Linking
on Derczynski
Summarizing and Exploring Tabular Data in Conversational Search
We propose to generate natural language summaries as answers to describe the complex information contained in a table.
Web Table Extraction, Retrieval and Augmentation: A Survey
Tables are a powerful and popular tool for organizing and manipulating data.
Novel Entity Discovery from Web Tables
We refer to this process as novel entity discovery and, to the best of our knowledge, it is the first endeavor on mining the unlinked cells in web tables.
Common Conversational Community Prototype: Scholarly Conversational Assistant
This paper discusses the potential for creating academic resources (tools, data, and evaluation approaches) to support research in conversational search, by focusing on realistic information needs and conversational interactions.
Auto-completion for Data Cells in Relational Tables
We address the task of auto-completing data cells in relational tables.
Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences
Studying the dialogues in one domain, we present a brief quantitative analysis of how people describe movie preferences at scale.
Unsupervised Context Retrieval for Long-tail Entities
Monitoring entities in media streams often relies on rich entity representations, like structured information available in a knowledge base (KB).
Recommending Related Tables
Tables are an extremely powerful visual and interactive tool for structuring and manipulating data, making spreadsheet programs one of the most popular computer applications.
NeuType: A Simple and Effective Neural Network Approach for Predicting Missing Entity Type Information in Knowledge Bases
Knowledge bases store information about the semantic types of entities, which can be utilized in a range of information access tasks.
Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval
Tables contain valuable knowledge in a structured form.
Impact of Training Dataset Size on Neural Answer Selection Models
It is held as a truism that deep neural networks require large datasets to train effective models.
Identifying Unclear Questions in Community Question Answering Websites
Thousands of complex natural language questions are submitted to community question answering websites on a daily basis, rendering them as one of the most important information sources these days.
IntentsKB: A Knowledge Base of Entity-Oriented Search Intents
We address the problem of constructing a knowledge base of entity-oriented search intents.
Generating Synthetic Data for Neural Keyword-to-Question Models
Since natural language questions are available in large quantities, we develop models to automatically generate the corresponding keyword queries.
Generating High-Quality Query Suggestion Candidates for Task-Based Search
We address the task of generating query suggestions for task-based search.
Towards an Understanding of Entity-Oriented Search Intents
Entity-oriented search deals with a wide variety of information needs, from displaying direct answers to interacting with services.
Ad Hoc Table Retrieval using Semantic Similarity
Specifically, we (i) represent queries and tables in multiple semantic spaces (both discrete sparse and continuous dense vector representations) and (ii) introduce various similarity measures for matching those semantic representations.
Towards Building a Knowledge Base of Monetary Transactions from a News Collection
We address the problem of extracting structured representations of economic events from a large corpus of news articles, using a combination of natural language processing and machine learning techniques.
On Type-Aware Entity Retrieval
Today, the practice of returning entities from a knowledge base in response to search queries has become widespread.
Generating Query Suggestions to Support Task-Based Search
We address the problem of generating query suggestions to support users in completing their underlying tasks (which motivated them to search in the first place).
