dc.contributor.author | Chen, Junnan | |
dc.date.accessioned | 2018-05-17 13:23:27 (GMT) | |
dc.date.available | 2018-05-17 13:23:27 (GMT) | |
dc.date.issued | 2018-05-17 | |
dc.date.submitted | 2018-05-11 | |
dc.identifier.uri | http://hdl.handle.net/10012/13301 | |
dc.description.abstract | Conversations depend on information from the context. To go beyond
one-round conversation, a chatbot must resolve contextual information such as:
1) co-reference resolution, 2) ellipsis resolution,
and 3) conjunctive relationship resolution.
There are simply not enough data to avoid these problems by trying to train a sequence-to-sequence
model for multi-round conversation similar to that of one-round conversation.
The contributions of this paper are: 1) We formulate the problem of
context resolution for conversation;
2) We present deep learning models, including
an end-to-end network for context resolution;
3) We propose a way of creating a huge amount of | en |
dc.language.iso | en | en |
dc.publisher | University of Waterloo | en |
dc.subject | nlp | en |
dc.subject | context resolution | en |
dc.subject | coreference | en |
dc.subject | deep learning | en |
dc.subject | deep neural networks | en |
dc.subject | dialog | en |
dc.subject | conversation understanding | en |
dc.title | Deep Context Resolution | en |
dc.type | Master Thesis | en |
dc.pending | false | |
uws-etd.degree.department | David R. Cheriton School of Computer Science | en |
uws-etd.degree.discipline | Computer Science | en |
uws-etd.degree.grantor | University of Waterloo | en |
uws-etd.degree | Master of Mathematics | en |
uws.contributor.advisor | Li, Ming | |
uws.contributor.affiliation1 | Faculty of Mathematics | en |
uws.published.city | Waterloo | en |
uws.published.country | Canada | en |
uws.published.province | Ontario | en |
uws.typeOfResource | Text | en |
uws.peerReviewStatus | Unreviewed | en |
uws.scholarLevel | Graduate | en |