REVIEW 2 cited by
Attention with Intention for a Neural Network Conversation Model
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
In a conversation or a dialogue process, attention and intention play intrinsic roles. This paper proposes a neural network based approach that models the attention and intention processes. It essentially consists of three recurrent networks. The encoder network is a word-level model representing source side sentences. The intention network is a recurrent network that models the dynamics of the intention process. The decoder network is a recurrent network produces responses to the input from the source side. It is a language model that is dependent on the intention and has an attention mechanism to attend to particular source side words, when predicting a symbol in the response. The model is trained end-to-end without labeling data. Experiments show that this model generates natural responses to user inputs.
Forward citations
Cited by 2 Pith papers
-
Teacher-Student Framework Enhanced Multi-domain Dialogue Generation
A multi-teacher, single-student distillation framework lets a multi-domain dialogue generator skip an external belief tracker at inference while still exploiting manually labeled dialogue states during training.
-
Deep Learning Based Chatbot Models
A 2017 student report surveys over 70 chatbot papers and reports preliminary Transformer experiments suggesting the model underperforms seq2seq on dialogue while speaker-addressee conditioning changes response quality.
Discussion (0). Continue with ORCID to comment.