Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy (2020)

Abstract: Knowledge-driven conversation approaches have achieved remarkable research attention recently. However, generating an informative response with multiple relevant knowledge without losing fluency and coherence is still one of the main challenges. To address this issue, this paper proposes a method that uses recurrent knowledge interaction among response decoding steps to incorporate appropriate knowledge. Furthermore, we introduce a knowledge copy mechanism using a knowledge-aware pointer network to copy words from external knowledge according to knowledge attention distribution. Our joint neural conversation model which integrates recurrent Knowledge-Interaction and knowledge Copy (KIC) performs well on generating informative responses. Experiments demonstrate that our model with fewer parameters yields significant improvements over competitive baselines on two datasets Wizard-of-Wikipedia(average Bleu +87%; abs.: 0.034) and DuConv(average Bleu +20%; abs.: 0.047)) with different knowledge formats (textual & structured) and different languages (English & Chinese).

## introduction dialogue systems have attracted much research attention in recent years. various end-to-end neural generative models based on the sequence-tosequence framework @xcite have been applied to the open-domain conversation and achieved impressive success in generating fluent dialog responses @xcite @xcite . however, many neural generative approaches from the last few years confined within utterances and responses, suffering from generating uninformative and inappropriate responses. to make responses more meaningful and expressive, several works on the dialogue sys-tem exploiting external knowledge. knowledgedriven methods focus on generating more informative and meaningful responses via incorporating structured knowledge consists of triplets @xcite @xcite or unstructured knowledge like documents @xcite @xcite . knowledge-based dialogue generation mainly has two methods: a pipeline way that deals with knowledge selection and generation successively @xcite , and a joint way that integrates knowledge selection into the generation process, for example, several works use memory network architectures @xcite to integrate the knowledge selection and generation jointly @xcite @xcite @xcite . the pipeline approaches separate knowledge selection from generation, resulting in an insufficient fusion between knowledge and generator. when integrating various knowledge, pipeline approaches lack flexibility. the joint method with the memory module usually uses knowledge information statically. the confidence of knowledge attention decreasing at decoding steps, which has the potential to produce inappropriate collocation of knowledge words. to generate informative dialogue response that integrates various relevant knowledge without losing fluency and coherence, this paper presents an effective knowledge-based neural conversation model that enhances the incorporation between knowledge selection and generation to produce more informative and meaningful responses. our model integrates the knowledge into the generator by using a recurrent knowledge interaction that dynamically updates the attentions of knowledge selection via decoder state and the updated knowledge attention assists in decoding the next state, which maintains the confidence of knowledge attention during the decoding process, it helps the decoder to fetch the latest knowledge information into the current decoding state. the generated words ameliorate the knowledge selection that refines the next word generation, and such repeated interaction between knowledge and generator is verified to be an effective way to integrate multiple knowledge coherently that to generate an informative and meaningful response when knowledge is fully taken account of. although recurrent knowledge interaction better solves the problem of selecting appropriate knowledge for generating the informative response, the preferable integration of knowledge into conversation generation still confronts an issue, i.e., it is more likely that the description words from external knowledge generated for the dialog response have a high probability of being an oov(out-ofvocabulary), which is a common challenge in natural language processing. a neural generative model with pointer networks has been shown to have the ability to handle oov problems @xcite . very few researches on copyable generative models pay attention to handle external knowledge, while in knowledge-driven conversation, the description words from knowledge are usually an important component of dialog response. thus, we leverage a knowledge-aware pointer network upon recurrent knowledge interactive decoder, which integrates the seq2seq model and pointer networks containing two pointers that refer to utterance attention distribution and knowledge attention distribution. we show that generating responses using the knowledge copy resolves the oov and the knowledge incompleteness problems. in summary, our main contributions are: (i) we propose a recurrent knowledge interaction, which chooses knowledge dynamically among decoding steps, integrating multiple knowledge into the response coherently. (ii) we use a knowledge-aware pointer network to do knowledge copy, which solves oov problem and keeps knowledge integrity, especially for long-text knowledge. (iii) the integration of recurrent knowledge interaction and knowledge copy results in more informative, coherent and fluent responses. (iv) our comprehensive experiments show that our model is general for different knowledge formats @xcite and different languages (english & chinese). furthermore, the results significantly outperform competitive baselines with fewer model parameters. ## model description given a dataset @xmath0, y i , k i )} n @xmath1, where n is the size of the dataset, a dialog response @xmath2, y 2 , . . . , y n } is produced by the conversation history utterance @xmath3, x 2 , . . . , x m }, using also the relative knowledge set @xmath4, k 2 , . . . , k s }. here, m and n are the numbers of tokens in the conversation history x and response y respectively, and s denotes the size of relevant knowledge candidates collection k. the relevant knowledge candidates collection k is assumed to be already provided and the size of candidates set is limited. each relevant knowledge element in candidate collection could be a passage or a triplet, denoted as @xmath5, κ 2 , . . . , κ l }, where l is the number of the tokens in the knowledge element. as illustrated in figure 1 , the model kic proposed in this work is based on an architecture involving an encoder-decoder framework @xcite and a pointer network @xcite . our model is comprised of four major components: (i) an lstm based utterance encoder; (ii) a general knowledge encoder suitable for both structural and documental knowledge; (iii) a recurrent knowledge interactive decoder; (iv) a knowledge-aware pointer network. ## related work conversation with knowledge incorporation has received considerable interest recently and is demonstrated to be an effective way to enhance performance. there are two main methods in knowledgebased conversation, retrieval-based approches @xcite and generation-based approaches. the generation-based method which achieves more research attention focuses on generating more informative and meaningful responses via incorporate generation with structured knowledge @xcite @xcite or documental knowledge @xcite . several works integrate knowledge and generation in the pipeline way, which deal with knowledge selection and generation separately. pipeline approaches pay more attention to knowledge selection, such as using posterior knowledge distribution to facilitate knowledge selection @xcite or used context-aware knowledge pre-selection to guide select knowledge @xcite . while various works entirety integration the knowledge with generation in an end-to-end way, which usually manage knowledge via external memory module. @xcite introduced a bag-of-words memory network and @xcite performed dialogue discussion with long-term memory. @xcite used a memory network to retrieve knowledge and combined with transformer architectures to generate responses. the pipeline approaches lack of flexibility as constricted by the separated knowledge selection, and the generation could not exploit knowledge sufficiently. the end-to-end approaches with memory module attention to knowledge statically, when integrating multiple knowledge into a response are easier to be confused. whereas we provide a recurrent knowledge interactive generator that sufficiently fusing the knowledge into generation to produce more informative dialogue responses. our work is also inspired by several works of text generation using copy mechanisms. @xcite used attention as a pointer to generate words from the input resource by index-based copy. @xcite incorporated copying into seq2seq learning to handle unknown words. @xcite introduced a hybrid pointer-generator that can copy words from the source text while retaining the ability to produce novel words. in task-oriented dialogue, the pointer networks were also used to improve copy accuracy and mitigate the common out-of-vocabulary problem @xcite . different from these works, we extend a pointer network referring to attention distribution of knowledge candidates that can copy words from knowledge resources and generate dialogue responses under the guidance of more complete description from knowledge. ## conclusion we propose a knowledge grounded conversational model with a recurrent knowledge interactive generator that effectively exploits multiple relevant knowledge to produce appropriate responses. meanwhile, the knowledge-aware pointer networks we designed allow copying important words, usually oov words, from knowledge. experimental results demonstrate that our model is powerful to generate much more informative and coherent responses than the competitive baseline models. in future work, we plan to analyze each turn of dialogue with reinforcement learning architecture, and to enhance the diversity of the whole dialogue by avoiding knowledge reuse.