K-ESIM and T-ESIM extend ESIM by incorporating domain knowledge and similar-dialog information, yielding preliminary accuracy gains on Ubuntu and Advising datasets for next-utterance selection.
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
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abstract
This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter. We also describe two neural learning architectures suitable for analyzing this dataset, and provide benchmark performance on the task of selecting the best next response.
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cs.CL 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Knowledge-incorporating ESIM models for Response Selection in Retrieval-based Dialog Systems
K-ESIM and T-ESIM extend ESIM by incorporating domain knowledge and similar-dialog information, yielding preliminary accuracy gains on Ubuntu and Advising datasets for next-utterance selection.