Active Learning and Multi-label Classification for Ellipsis and Coreference Detection in Conversational Question-Answering
In human conversations, ellipsis and coreference are commonly occurring linguistic phenomena. Although these phenomena are a mean of making human-machine conversations more fluent and natural, only few dialogue corpora contain explicit indications on which turns contain ellipses and/or coreferences. In this paper we address the task of automatically detecting ellipsis and coreferences in conversational question answering. We propose to use a multi-label classifier based on DistilBERT. Multi-label classification and active learning are employed to compensate the limited amount of labeled data. We show that these methods greatly enhance the performance of the classifier for detecting these phenomena on a manually labeled dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
Active LearningConversational Question AnsweringMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONQuestion AnsweringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling
In order to alleviate the shortage of multi-domain data and to capture discourse phenomena for task-oriented dialogue modeling, we propose RiSAWOZ, a large-scale multi-domain Chinese Wizard-of-Oz dataset with Rich Semant…
Dialogue State TrackingIntent DetectionNatural Language Understandingslot-filling+2Ellipsis Resolution as Question Answering: An Evaluation
Most, if not all forms of ellipsis (e.g., so does Mary) are similar to reading comprehension questions (what does Mary do), in that in order to resolve them, we need to identify an appropriate text span in the preceding …
coreference-resolutionCoreference ResolutionMachine Reading ComprehensionQuestion Answering+1Adapting Coreference Resolution Models through Active Learning
Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active l…
Active LearningClusteringcoreference-resolutionCoreference ResolutionSpoken Conversational Search for General Knowledge
We present a spoken conversational question answering proof of concept that is able to answer questions about general knowledge from Wikidata. The dialogue component does not only orchestrate various components but also …
Conversational Question AnsweringConversational SearchGeneral KnowledgeQuestion AnsweringDialogSum: A Real-Life Scenario Dialogue Summarization Dataset
Proposal of large-scale datasets has facilitated research on deep neural models for news summarization. Deep learning can also be potentially useful for spoken dialogue summarization, which can benefit a range of real-li…
Abstractive Dialogue SummarizationCommon Sense ReasoningManagementRepresentation Learning+1