16 research outputs found
Better Conversations by Modeling,Filtering,and Optimizing for Coherence and Diversity
We present three enhancements to existing encoder-decoder models for
open-domain conversational agents, aimed at effectively modeling coherence and
promoting output diversity: (1) We introduce a measure of coherence as the
GloVe embedding similarity between the dialogue context and the generated
response, (2) we filter our training corpora based on the measure of coherence
to obtain topically coherent and lexically diverse context-response pairs, (3)
we then train a response generator using a conditional variational autoencoder
model that incorporates the measure of coherence as a latent variable and uses
a context gate to guarantee topical consistency with the context and promote
lexical diversity. Experiments on the OpenSubtitles corpus show a substantial
improvement over competitive neural models in terms of BLEU score as well as
metrics of coherence and diversity
Response Generation by Context-aware Prototype Editing
Open domain response generation has achieved remarkable progress in recent
years, but sometimes yields short and uninformative responses. We propose a new
paradigm for response generation, that is response generation by editing, which
significantly increases the diversity and informativeness of the generation
results. Our assumption is that a plausible response can be generated by
slightly revising an existing response prototype. The prototype is retrieved
from a pre-defined index and provides a good start-point for generation because
it is grammatical and informative. We design a response editing model, where an
edit vector is formed by considering differences between a prototype context
and a current context, and then the edit vector is fed to a decoder to revise
the prototype response for the current context. Experiment results on a large
scale dataset demonstrate that the response editing model outperforms
generative and retrieval-based models on various aspects