177 research outputs found
Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia
Only few digital libraries and reference managers offer recommender systems,
although such systems could assist users facing information overload. In this
paper, we introduce Mr. DLib's recommendations-as-a-service, which allows third
parties to easily integrate a recommender system into their products. We
explain the recommender approaches implemented in Mr. DLib (content-based
filtering among others), and present details on 57 million recommendations,
which Mr. DLib delivered to its partner GESIS Sowiport. Finally, we outline our
plans for future development, including integration into JabRef, establishing a
living lab, and providing personalized recommendations.Comment: Accepted for publication at the JCDL conference 201
An Annotated Corpus of Reference Resolution for Interpreting Common Grounding
Common grounding is the process of creating, repairing and updating mutual
understandings, which is a fundamental aspect of natural language conversation.
However, interpreting the process of common grounding is a challenging task,
especially under continuous and partially-observable context where complex
ambiguity, uncertainty, partial understandings and misunderstandings are
introduced. Interpretation becomes even more challenging when we deal with
dialogue systems which still have limited capability of natural language
understanding and generation. To address this problem, we consider reference
resolution as the central subtask of common grounding and propose a new
resource to study its intermediate process. Based on a simple and general
annotation schema, we collected a total of 40,172 referring expressions in
5,191 dialogues curated from an existing corpus, along with multiple judgements
of referent interpretations. We show that our annotation is highly reliable,
captures the complexity of common grounding through a natural degree of
reasonable disagreements, and allows for more detailed and quantitative
analyses of common grounding strategies. Finally, we demonstrate the advantages
of our annotation for interpreting, analyzing and improving common grounding in
baseline dialogue systems.Comment: 9 pages, 7 figures, 6 tables, Accepted by AAAI 202
A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context
Common grounding is the process of creating, repairing and updating mutual
understandings, which is a critical aspect of sophisticated human
communication. However, traditional dialogue systems have limited capability of
establishing common ground, and we also lack task formulations which introduce
natural difficulty in terms of common grounding while enabling easy evaluation
and analysis of complex models. In this paper, we propose a minimal dialogue
task which requires advanced skills of common grounding under continuous and
partially-observable context. Based on this task formulation, we collected a
largescale dataset of 6,760 dialogues which fulfills essential requirements of
natural language corpora. Our analysis of the dataset revealed important
phenomena related to common grounding that need to be considered. Finally, we
evaluate and analyze baseline neural models on a simple subtask that requires
recognition of the created common ground. We show that simple baseline models
perform decently but leave room for further improvement. Overall, we show that
our proposed task will be a fundamental testbed where we can train, evaluate,
and analyze dialogue system's ability for sophisticated common grounding.Comment: AAAI 201
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