TITLE: Conversational Entity Linking: Problem Definition and Datasets 

ABSTRACT: Machine understanding of user utterances in conversational systems is of
utmost importance for enabling engaging and meaningful conversations with
users. Entity Linking (EL) is one of the means of text understanding, with
proven efficacy for various downstream tasks in information retrieval. In this
paper, we study entity linking for conversational systems. To develop a better
understanding of what EL in a conversational setting entails, we analyze a
large number of dialogues from existing conversational datasets and annotate
references to concepts, named entities, and personal entities using
crowdsourcing. Based on the annotated dialogues, we identify the main
characteristics of conversational entity linking. 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. The resources released with this paper include
annotated datasets, detailed descriptions of crowdsourcing setups, as well as
the annotations produced by various EL systems. These new resources allow for
an investigation of how the role of entities in conversations is different from
that in documents or isolated short text utterances like queries and tweets,
and complement existing conversational datasets.
Machine understanding of user utterances in conversational systems is of
utmost importance for enabling engaging and meaningful conversations with
users. Entity Linking (EL) is one of the means of text understanding, with
proven efficacy for various downstream tasks in information retrieval. In this
paper, we study entity linking for conversational systems. To develop a better
understanding of what EL in a conversational setting entails, we analyze a
large number of dialogues from existing conversational datasets and annotate
references to concepts, named entities, and personal entities using
crowdsourcing. Based on the annotated dialogues, we identify the main
characteristics of conversational entity linking. 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. The resources released with this paper include
annotated datasets, detailed descriptions of crowdsourcing setups, as well as
the annotations produced by various EL systems. These new resources allow for
an investigation of how the role of entities in conversations is different from
that in documents or isolated short text utterances like queries and tweets,
and complement existing conversational datasets.
Conversational Entity Linking: Problem Definition and Datasets
http://arxiv.org/abs/2105.04903v1