Approximation of Response Knowledge Retrieval in Knowledge-grounded Dialogue Generation (2020)

Abstract: This paper is concerned with improving dialogue generation models through injection of knowledge, e.g., content relevant to the post that can increase the quality of responses. Past research extends the training of the generative models by incorporating statistical properties of posts, responses and related knowledge, without explicitly assessing the knowledge quality. In our work, we demonstrate the importance of knowledge relevance and adopt a two-phase approach. We first apply a novel method, Transformer & Post based Posterior Approximation (TPPA) to select knowledge, and then use the Transformer with Expanded Decoder (TED) model to generate responses from both the post and the knowledge. TPPA method processes posts, post related knowledge, and response related knowledge at both word and sentence level. Our experiments with the TED generative model demonstrate the effectiveness of TPPA as it outperforms a set of strong baseline models. Our TPPA method is extendable and supports further optimization of knowledge retrieval and injection.

## introduction in recent years, there have been concerted efforts to model dialogue interactions and generate an appropriate response to an initial user statement, referred to as a post. research has led to generative models, e.g., sequence-to-sequence @xcite ) and transformer @xcite , that produce reasonable responses using the original post solely during the generation process. recent studies @xcite @xcite explored more realistic dialogue models that include knowledge related to the posts, typically a collection of sentences that refer to the topics in the posts and responses. consequently, the response generation wiz post: yep. you've got to select for safety standards, of course, but when you're designing at a mercedes level the folks buying those cars are going to expect a certain standard of comfort, too! wiz response: especially, i think consumers expect great in formula one, highest class auto racing. tppa (top 1): formula one (also formula 1 or f1 and officially the fia formula one world championship) is the highest class of single seat auto racing that is sanctioned by the federation internationale de l'automobile (fia). tppa (top 2): stock car racing is a form of automobile racing found mainly and most prominently in the united states and canada, with australia, new zealand and brazil also having forms of stock car auto racing. prk (top 1): mercedes is part of the mcqueen family and is the longest serving mcqueen on the series. prk (top 2): he also won races in midget cars, and sprint cars. rrk (top 1): formula one (also formula 1 or f1 and officially the fia formula one world championship) is the highest class of single seat auto racing that is sanctioned by the federation internationale de l'automobile (fia). rrk (top 2): the fia formula one world championship has been one of the premier forms of racing around the world since its inaugural season in 1950. evaluation of such approaches has shown that the knowledge based on posts alone may lack focus, i.e., may exhibit topic drifts and thus introduce noise. in practice, however, the key challenge is to implement an effective selection of response related knowledge, considering that the responses to posts are not observed during dialogue generation. in this paper, we present the transformer & post based posterior approximation (tppa) method that achieves that by applying multi-stage processing of posts, post related knowledge and response related knowledge to capture word and sentence level characteristics (through word embeddings, transformer and max-pooling), that can be useful for ranking and selecting knowledge of new posts during the test phase. most important contributions of our work are: in addition, the separation of the knowledge selection from the generative models offers maximum flexibility for integrating and exploring alternative retrieval models and knowledge representations. we make our codes publicly available at https://github.com/tonywenuon/emnlp2020 tppa. ## related work in this section we first discuss retrieval models and then knowledge injection into generative models. retrieval models. most traditional retrieval models, such as bm25 @xcite , are unsupervised methods, relying on lexical matching between query terms and document text using different weighting and normalization schemes. in contrast, recent studies use neural ranking models, such as deep structured semantic models (dssm) @xcite , weakly supervised neural ranking models @xcite and jointly trained neural models @xcite . they are built to respond to information needs represented by a query. we illustrate our approach by adopting bm25 for initial retrieval of relevant knowledge. we also use the post related results to create an extended representation of the post, similar to the pseudo-relevancefeedback in query-based search @xcite . ## tppa method in this section, we describe the architecture and the process of selecting knowledge using the tppa method. figure 2 depicts three tppa components: the number of negative examples m is selected empirically, to avoid overfitting. tppa operation consists of phase 1: training phase that utilizes training data (p, r, k p ) to train all the three components of the system based on known responses r; and phase 2: test phase during which individual post-knowledge samples (p, k p ) are processed in order to arrive at a selection of knowledge (@xmath0. ## experiments our approach for knowledge injection separates the knowledge selection from the response generation models. we, thus, evaluate tppa in terms of (1) precision in selecting relevant knowledge for a given post, judged by whether the rrk 1 can be ranked within top n position, and (2) effectiveness of the retrieved knowledge when injected into a response generation model. ## conclusions and discussions our investigations of the knowledge associated with post-response pairs lead us valuable insights into how well selected response-retrieved knowledge rrk can improve the performance of the generative models. considering that response is not observable in the test phase, we developed a tppa method that selects knowledge items by the careful embedding of the knowledge and optimized representation of the post and post-related knowledge prk. we empirically demonstrate the superiority of tppa, and being separated from the generative models. this provides flexibility to explore alternative components and models. despite its effectiveness, we now discuss one potential limitation of our tppa model.we find that the quality of the knowledge base has a huge impact on the effectiveness of tppa. the wiz and holl-e we experiment with are two data sets from which candidate knowledge items are of high quality and manually selected. as shown in figure 1 for the wiz dataset, rrk 1 group contains on average more than two common words than prk 1 group that would help to constitute the ground truth response. the same trend also holds for the holl-e data set. however, when looking at the reddit data set foot_3 , as shown in figure 3 , we find that rrk 1 group and prk 1 group almost contain the same number of common words, compared to the ground-truth response. this is not surprising given the nature of this dataset: reddit is an online forum where each post is typically initiated with a url to a web page (grounding) that defines the topic of the post, provided by the author. however, the repliers of the post might not read that information at all and respond according to their own knowledge. empirically, we find tppa can not benefit from the knowledge under this circumstance and perform worse than the baselines. this implies that when knowledge is potential of low quality, using prk as the source of evidence for pseudo relevance feedback can result in potential topic drift. in future work, we would like to (1) make tppa more robust irrespective of the quality of provided knowledge; (2) develop an end-to-end model that directly model response generation with the help of response-related knowledge.