1,480 research outputs found
Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering
Community question answering (CQA) gains increasing popularity in both
academy and industry recently. However, the redundancy and lengthiness issues
of crowdsourced answers limit the performance of answer selection and lead to
reading difficulties and misunderstandings for community users. To solve these
problems, we tackle the tasks of answer selection and answer summary generation
in CQA with a novel joint learning model. Specifically, we design a
question-driven pointer-generator network, which exploits the correlation
information between question-answer pairs to aid in attending the essential
information when generating answer summaries. Meanwhile, we leverage the answer
summaries to alleviate noise in original lengthy answers when ranking the
relevancy degrees of question-answer pairs. In addition, we construct a new
large-scale CQA corpus, WikiHowQA, which contains long answers for answer
selection as well as reference summaries for answer summarization. The
experimental results show that the joint learning method can effectively
address the answer redundancy issue in CQA and achieves state-of-the-art
results on both answer selection and text summarization tasks. Furthermore, the
proposed model is shown to be of great transferring ability and applicability
for resource-poor CQA tasks, which lack of reference answer summaries.Comment: Accepted by AAAI 2020 (oral
Access to recorded interviews: A research agenda
Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed
Answering clinical questions with knowledge-based and statistical techniques
The combination of recent developments in question-answering research and the availability of unparalleled resources developed specifically for automatic semantic processing of text in the medical domain provides a unique opportunity to explore complex question answering in the domain of clinical medicine. This article presents a system designed to satisfy the information needs of physicians practicing evidence-based medicine. We have developed a series of knowledge extractors, which employ a combination of knowledge-based and statistical techniques, for automatically identifying clinically relevant aspects of MEDLINE abstracts. These extracted elements serve as the input to an algorithm that scores the relevance of citations with respect to structured representations of information needs, in accordance with the principles of evidencebased medicine. Starting with an initial list of citations retrieved by PubMed, our system can bring relevant abstracts into higher ranking positions, and from these abstracts generate responses that directly answer physicians â questions. We describe three separate evaluations: one focused on the accuracy of the knowledge extractors, one conceptualized as a document reranking task, and finally, an evaluation of answers by two physicians. Experiments on a collection of real-world clinical questions show that our approach significantly outperforms the already competitive PubMed baseline. 1
- âŚ