MTGP : Multi-turn Target-oriented Dialogue Guided by Generative Global Path with Flexible Turns (2023)

Abstract: Target-oriented dialogue guides the dialogue to a target quickly and smoothly. The latest approaches focus on global planning, which plans toward the target before the conversation instead of adopting a greedy strategy during the conversation. However, the global plan in existing works is fixed to certain turns by generating paths with certain nodes, which limits the optimization of turns and coherence of the target-oriented process. Toward flexible global planning, we propose to generate a global path as a natural language sentence instead of a sequence of nodes. With this path, the dialog is guided to the target with flexible turns of dialog. For model training, we also extract targetoriented dialogues from the chit-chat corpus with a knowledge graph. We conduct experiments on three datasets and simulate scenarios with and without user participation. The results show that our method has fewer turns, more coherent semantics, and a higher success rate in reaching the target than baselines.

## introduction open-domain dialogue agents generate responses using large pre-trained language models and have a fluent multi-turn dialogue with users. it focuses on chit-chat, mostly responding passively to users. more often, we prefer the agent to guide the transition of a topic during dialogue proactively. targetoriented dialogue is based on open-domain dialogue, which can actively guide the dialogue while communicating with the user fluently. it has many application scenarios, e.g., psychological counseling, dialogue recommendation, and education. in target-oriented dialogue, we hope the dialogue agent can proactively guide the conversation to a goal with coherent semantics and fewer turns. the definition of the goal can be various. for example, @xcite set the goal as a sentence, and @xcite set the goal as a specific keyword. following the definition of @xcite to the task, we set the goal as a concept word. the task succeeds when a user or agent mentions the target word naturally during the dialogue. previous approaches simplify target-oriented dialogue tasks, and they predict the next-turn keywords based on the dialogue history. moreover, it's essential to model logical context. therefore, many works combine predicting keywords with the common knowledge graph @xcite @xcite and then generate the next-turn response by retrieval or generation. current research concentrates more on global planning for a global target. @xcite first generates a global path connecting the source and target words. then, guided by this global path, they generate a more coherent one-turn dialogue by generating bridging utterances. @xcite plans a global dialogue path based on the knowledge graph and adjusts the model to adapt to the global goal through reinforcement learning. although the existing target-oriented dialogue methods have shown promising results in success rate and coherence, there are some issues to be solved. the traditional target-oriented dialogue models only consider the dialogue context in predicting the next-turn keywords and do not explicitly plan a global path for the global target. however, @xcite describes the targetoriented dialogue task, and their experiments show that global planning is an effective method for target-oriented dialogue. to introduce global planning to target-oriented dialogue, some existing global planning methods such as @xcite . @xcite use a static knowledge graph to retrieve a global path. however, a static knowledge graph is still insufficient to track the logic path in target-oriented dialogue. we know that human conversation is complex, some transitions between concept words are plausible in human dialogue, but they are not connected in the graph. @xcite and @xcite use a generative model to generate paths between two source and target words. this method combines the characteristics of pre-trained language models and can generate paths that are more relevant to a given source and target words. it is not only simple retrieval but the ability to summarize facts in a knowledge graph and connect relations that may not exist in the graph. however, @xcite focuses on generating bridging sentences which means only one-turn dialogue is performed. and current research on global targetoriented dialogue can only generate fixed dialogue turns according to the global path. to address these issues, we propose multi-turn target-oriented dialogue guided by generative global path(mtgp) which generates a global path using generative model, and guide multi-turn dialogue through the path. we first generate a global path as a natural language sentence, which connects the concepts in the source context and global target. then we train a response generator on the sampled dialogue corpus. finally, we use the generated path to guide dialogue generation. in particular, we do not strictly limit the turns to achieve the target and complete the dialogue within six turns, so our model will generate multi-turn conversation with uncertain turns. furthermore, we propose an atomic processing method of dialogue corpus for the multi-turn target-oriented task. due to the lack of suitable multi-turn targetoriented dialogue datasets, we must achieve data requirements through automatic processing. the existing chit-chat corpus can't be directly used as training data for our task because the target is not explicitly set, and transition words in the dialogue are not labeled. but we can extract a corpus that meets the target-oriented task. specifically, we match the dialogue corpus with the knowledge graph conceptnet @xcite and only extract the dialogue corpus that can find a clear turning path in the graph. besides, the endpoint of the path is set to a target word. these data will be used as the training data of the dialogue generation model. the purpose is to learn the turning in the real dialogue. at the same time, the model can also ensure that the concept words in the path will also appear in the generated responses. we extract multi-turn dialogue corpus from two large-scale chit-chat datasets, dailydialog @xcite , and convai2 @xcite . after generating an explicit commonsense path from source to target in the final dialogue generation stage, we follow this path for multi-turn dialogue. in this way, we learn from real human dialogue corpus and follow word transitions to achieve a smooth transition. the path generated by the pretrained language model within a limited number of hops also ensures that our multi-turn dialogue can reach the target faster. our method performs well within existing baselines in both success rate and coherence. in addition, according to tgcp @xcite , we also try to generate multi-turn dialogue without users. meanwhile, we have some experiments on one-turn dialogues using otters @xcite . we summarize our contributions as follows: there is also some previous work on targetoriented dialogue predicting the next-turn of keywords and retrieving responses @xcite @xcite . these work do not have global planning but only set up a global tar-figure 1 : the upper part of the model illustration represents the process of prepossessing and training pg and nrg, which mainly includes generating the sampled paths, and the process of automatically extracting dialogue corpus proposed by us. the sampled paths are used for training pg and the sampled dialog corpus are used for training nrg, pg and nrg are both based on gpt-2. the lower part of the model illustration is the process of using the model for multi-turn dialogue. first, input the source and target words into pg, and a path is output, and then input target, path, context into nrg to generate a reply. note that the path is entered incrementally. one problem of target-oriented dialogue studies is the datasets. there is a one-turn dialogue corpus named otters @xcite , which is suitable for target-oriented dialogue tasks, but the dataset is still small. coda uses otters and data augmentation methods to construct a one-turn dialogue model that generates bridging sentences. topkg proposes a method to extract dialogue materials that meet the requirements of target-oriented dialogue from a small talk corpus. commonsense knowledge for target-oriented dialogue systems. for target-oriented dialogue, we need to reach the global target faster and need context transitions in context to be coherent and logical. naturally, we use commonsense graphs as external knowledge for global planning and dialogue generation. for example, @xcite al. (2020) ## task overview given a context u and a target word t c , we first extract concept word from u as u c , and then generate a path to connect the u c and t c . the path is consists of relations @xmath0, ..., r k } and entity words then we convert it into a semantic sentence p ath. our task is to guide multi-turn dialogue generation with the p ath. for t-th turn generated sentence, it should contain the e t in the p ath. naturally, the dialogue ends with the user or agent mentioning the target word or phrase, while the process is fluent and takes a few turns. ## model we present the mtgp model, depicted in 1. our approach involves two main components: a path generator (pg) model trained on paths sampled from conceptnet through random walks, and a next-turn response generator (nrg) trained on dialogues extracted from a chit-chat corpus enriched with knowledge paths from conceptnet. during inference, we use pg and nrg to generate responses that reflect both global paths from pg and the context of the conversation. ## conclusions in this work, we propose a multi-turn targetoriented dialogue model which can achieve global target guiding by a generative global path. we also offer an automatic method to match dialogue corpus with commonsense graphs and sample dialogue corpus with paths. the extensive experiments show that our approach can make the dialogue simple and effective to reach the target with a higher success rate. in future work, we will explore using unstructured knowledge for global planning.