Combining Argumentation Structure and Language Model for Generating Natural Argumentative Dialogue (2022)

Abstract: Argumentative dialogue is an important process where speakers discuss a specific theme for consensus building or decision making. In previous studies for generating consistent argumentative dialogue, retrieval-based methods with hand-crafted argumentation structures have been used. In this study, we propose a method to generate natural argumentative dialogues by combining an argumentation structure and language model. We trained the language model to rewrite a proposition of an argumentation structure on the basis of its information, such as keywords and stance, into the next utterance while considering its context, and we used the model to rewrite propositions in the argumentation structure. We manually evaluated the generated dialogues and found that the proposed method significantly improved the naturalness of dialogues without losing consistency of argumentation.

## introduction argumentative dialogue is an important process where speakers discuss a specific theme for building consensus or making decisions @xcite . the method to automatically generate argumentative dialogues not only contributes to the realization of such a dialogue system but can also provide us with content that can give us insights regarding the theme. in previous studies in argumentation generation, retrieval-based methods with a hand-crafted argumentation structure consisting of propositions written in natural sentences were used for generating consistent argumentative dialogue @xcite @xcite @xcite . however, these methods output propositions as utterances as they are; thus the previous context is not considered, making the generated dialogue less coherent. in addition, although generation-based methods for argumentation with language models have also been proposed, generation of natural and consistent dialogue has never been investigated @xcite @xcite . in this study, we propose a method to generate natural argumentative dialogue by combining an argumentation structure and a language model as illustrated in figure 1 . specifically, we propose a method to rewrite propositions of an argumentation structure into natural utterances. the method generates natural utterances on the basis of the context of dialogue and propositions' key information, such as keywords and a stance. we manually evaluated the generated dialogues and found that the proposed method significantly improved the naturalness of dialogues without losing consistency of argumentation. ## related work our approach is related to retrieval-based generation, which generates responses by referring to the examples retrieved from resources, and keyword-based generation, which ensures that specified contents such as keywords are included in generated answers. retrieval-based generation has been applied to a wide range of tasks, such as question answering @xcite , dialogue modeling @xcite , and story generation @xcite in addition to argumentation generation. our work is different from these studies in that we aim to improve the naturalness of argumentative dialogue while maintaining consistency by using a language model with argumentation structures. as far as we know, no previous work has tackled the problem of generating argumentative dialogue by using both pre-trained language models and argumentation structures. keyword-based generation is proposed for introducing contents specified with keywords into generated utterances @xcite . in addition to the content's keywords, the methods have been proposed for controlling an utterance topic by incorporating an emotional keyword @xcite and topical keywords @xcite . in addition, the methods have also been proposed for generating an utterance that exactly includes the given keywords @xcite . these studies do not focus on argumentation in which logical consistency and the stance of a speaker need to be considered. ## datasets we first briefly present the datasets we use in this study. we use two kinds of datasets: argumentation structure @xcite and argumentative dialogue corpus @xcite . the argumentation structure is the source of the argumentative dialogue, which is a tree-like structure of logically connected propositions. the argumentative dialogues are used for fine-tuning the language model to rewrite the propositions into utterances while considering their previous context. the datasets are in japanese. the argumentation structure contains propositions in a specific theme (e.g., "are you for vs. against autonomous cars?") as shown at the top of figure 1 @xcite . the resource is a tree where each proposition corresponds to a node written in a natural sentence and its relationships correspond to edges. the argumentation structure is constructed in five argumentation themes. the depth of trees is six, and each tree has 2,255 nodes on average. the argumentative dialogue corpus was constructed by @xcite in the same five themes as the argumentation structure. speakers took opposite stances (e.g., for or against) and conducted argumentation to persuade their counterpart. they did not refer to the argumentation structure; thus there is no exact correspondence between an utterance in the dialogue and a proposition in the argumentation structure. since each speaker has a stance, each utterance of a speaker is regarded as having the stance of that speaker. in addition, @xcite manually labeled the argumentation-related dialogue acts (assertion, question, concession, retraction, and other) to each utterance in the corpus. the corpus has 250 dialogues (17,804 utterances in total and 71 utterances per dialogue). ## proposed method our idea for generating an argumentative dialogue is to first create a scenario on the basis of the graph (a sequence of propositions) and then convert that graph into an argumentative dialogue. the problem is how to convert each proposition into a naturalistic utterance. for this, we use keywordsbased generation in which we utilize key information about a proposition to generate an utterance. through the investigation of the datasets, we identified the following key information. in fine-tuning the model, the context before each utterance is used as input, and each utterance is used as output. the model is fine-tuned so that, given the context and key information of the output utterance, the model can reconstruct the utterance. this is in the hope that when the same information is given from a proposition, a natural utterance for the proposition can be generated. a full example of an input used in figure 2 is the following. autonomous cars:[sep][spk1]hello.[sep] [spk2]what do you think about autonomous cars?[sep][spk1]i think it is good.[sep] stance:support,da:assertion,turn:04[sep] keywords:autonomous,cars,reduce,accidents each element is divided with a separator @xcite . the first element shows an argumentation theme. the context including three utterances at maximum follows. then, a stance, dialogue act, turn number, and keywords are listed. we used the stance, dialogue act, and turn number labeled in the argumentative dialogue corpus for creating the training data in fine-tuning the model. the keywords are automatically extracted through a keyword extractor where a part-of-speech tagger is applied to an utterance in a dialogue in order to obtain all content words as keywords. the insertion of these kinds of information seems simple but has been reported to be effective in previous studies @xcite @xcite . the argumentative dialogue is generated in the following manner. first, by randomly selecting the path of an argumentation structure, we create a sequence of propositions as a source scenario of generated argumentative dialogue @xcite . then, the model rewrites the proposition into an output utterance using the fine-tuned language model from the top proposition to the bottom one. the generated utterance is added to the context for generating the next utterance from the next proposition. note that the keywords are extracted from the proposition with the same keyword extractor used in fine-tuning. the dialogue act, stance, and turn number are predetermined by the scenario; each speaker's stance is fixed (e.g., a speaker a for autonomous cars and a speaker b against it) and dialogue act is determined by heuristic rules to realize a typical flow of argumentation (e.g., the first utterance is question and the second one is assertion) as will be explained in section 5.2. ## experiments we manually evaluated the dialogues generated from the proposed method. we conducted a static evaluation of dialogues by crowdsourcing, which is often used to evaluate dialogue generation in dialogue systems @xcite . ## conclusion this study proposed a method to generate natural argumentative dialogue by combining an argumentation structure and language model. we proposed the method to fine-tune the language model to rewrite propositions of an argumentation structure into a natural argumentative dialogue on the basis of their key information, such as keywords and stance, into the next utterance while considering its context. the proposed method significantly improved the naturalness of dialogues without losing the consistency of argumentation. future work includes conducting a live evaluation of the proposed method and validating the effectiveness of the proposed method in other experimental settings using other pre-trained language models and datasets. in addition, the naturalness of generated dialogues needs to be improved by automatically generating more naturalistic dialogue scenarios by using a language model.