There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning (2022)

Abstract: Knowledge-grounded dialogue (KGC) shows excellent potential to deliver an engaging and informative response. However, existing approaches emphasize selecting one golden knowledge given a particular dialogue context, overlooking the one-to-many phenomenon in dialogue. As a result, existing paradigm limits the diversity of knowledge selection and generation. To this end, we establish a multi-reference KGC dataset and propose a series of metrics to systematically assess the one-to-many efficacy of existing KGC models. Furthermore, to extend the hypothesis space of knowledge selection to enhance the mapping relationship between multiple knowledge and multiple responses, we devise a span-based variational model and optimize the model in a wake-sleep style with an ameliorated evidence lower bound objective to learn the one-to-many generalization. Both automatic and human evaluations demonstrate the efficacy of our approach.

## introduction maintaining appropriate human-computer dialogue is an important task leaping toward advanced artificial intelligence and external knowledge is a key ingredient to engaging and meaningful responses @xcite . to this end, the research area of knowledge-grounded conversation (kgc) has been explored with great interest. in recent years, a number of methods @xcite @xcite and benchmarks @xcite have been proposed. these methods mainly follow the two-step paradigm proposed by @xcite : given a dialogue context and a candidate knowledge pool, † the first two authors contribute equally. xueliang zhao is responsible for the design of the methodology and algorithm. tingchen fu is responsible for the implementation and experiment. the order is decided by a coin flip. there is no blue pigmentation in blue eyes. the eyes appear blue as a result of rayleigh scattering, the same process that makes the sky appear blue. response r1: daddy why is the sky blue? same reason your eyes are, son . r2: it sounds really stupid to me to say ' the sky isn't actually lllllblue, but dyed by rayleigh scattering of sunlinght or something ||||like that. i mean, the sky is blue. it's blue, guys. r3: i even know someone whose two eyes in different colors, they lllllcall it heterochromia. do they have no pigmentation in eyes as lllllwell? r4: is it affected by generic and inheritable from parents? k1: this would be analogous to the change in the color of the sky , llllllfrom the blue given by the rayleigh scattering of sunlight by llllllsmall gas molecules when the sky is clear, to the gray caused by llllllmie scattering of large water droplets when the sky is cloudy. k2:heterochromia of the eye is called heterochromia llllliridum or heterochromia iridis. k3:for example, the film actor lee van cleef was born with one blue llllleye and one green eye, a trait that reportedly was common in lllllhis family, suggesting that it was a genetic trait. ## related work our work is in line with the research of knowledgegrounded conversation, whose goal is to generate informative responses with external knowledge @xcite @xcite . since existing benchmarks usually only contain one reference for a conversation @xcite @xcite , most previous works take the assumption of golden knowledge @xcite , and some of them use hindsight information from response to detect the golden knowledge @xcite @xcite , omitting all the other unobserved but plausible responses. besides, the granularity of grounding is limited to a complete sentence or passage. recently, some researchers have attempted to explore the possibility of grounding dialogue with span @xcite @xcite . their spans are deterministic from hard selection process. differently, we view the span prediction as a probabilistic process and propose a variational method to capture the attention span. the proposed model also relates to the one-tomany property in dialogue, referring to the phenomenon that the multiple responses are proper for a single dialogue context. how to train and evaluate the one-to-many generalization of a dialogue system is a widely studied topic in opendomain response generation @xcite @xcite . inspired by the efficacy of variational auto-encoder (vae), some previous works resort to latent variables to model the one-to-many property of dialogue. for ## conclusions we have shown that the proposed variational knowledge attention method is helpful to ground a dialogue flexibly at different levels of granularity. besides, we devise a wake-sleep style learning algorithm to adapt the original elbo. and to enhance the mapping relationship between different spans and different responses, we ameliorate the original reward in reinforce @xcite to adapt to the multi-reference scenario. we have demonstrated the efficacy of our model with extensive experiments.