26,753 research outputs found
Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network
Neural conversational models learn to generate responses by taking into
account the dialog history. These models are typically optimized over the
query-response pairs with a maximum likelihood estimation objective. However,
the query-response tuples are naturally loosely coupled, and there exist
multiple responses that can respond to a given query, which leads the
conversational model learning burdensome. Besides, the general dull response
problem is even worsened when the model is confronted with meaningless response
training instances. Intuitively, a high-quality response not only responds to
the given query but also links up to the future conversations, in this paper,
we leverage the query-response-future turn triples to induce the generated
responses that consider both the given context and the future conversations. To
facilitate the modeling of these triples, we further propose a novel
encoder-decoder based generative adversarial learning framework, Posterior
Generative Adversarial Network (Posterior-GAN), which consists of a forward and
a backward generative discriminator to cooperatively encourage the generated
response to be informative and coherent by two complementary assessment
perspectives. Experimental results demonstrate that our method effectively
boosts the informativeness and coherence of the generated response on both
automatic and human evaluation, which verifies the advantages of considering
two assessment perspectives.Comment: Accepted by AAAI 202
Dialogue Coherence Assessment Without Explicit Dialogue Act Labels
Recent dialogue coherence models use the coherence features designed for
monologue texts, e.g. nominal entities, to represent utterances and then
explicitly augment them with dialogue-relevant features, e.g., dialogue act
labels. It indicates two drawbacks, (a) semantics of utterances is limited to
entity mentions, and (b) the performance of coherence models strongly relies on
the quality of the input dialogue act labels. We address these issues by
introducing a novel approach to dialogue coherence assessment. We use dialogue
act prediction as an auxiliary task in a multi-task learning scenario to obtain
informative utterance representations for coherence assessment. Our approach
alleviates the need for explicit dialogue act labels during evaluation. The
results of our experiments show that our model substantially (more than 20
accuracy points) outperforms its strong competitors on the DailyDialogue
corpus, and performs on par with them on the SwitchBoard corpus for ranking
dialogues concerning their coherence.Comment: Accepted at ACL 202
- …