A Large Collection of Model-generated Contradictory Responses for Consistency-aware Dialogue Systems (2024)

Abstract: Mitigating the generation of contradictory responses poses a substantial challenge in dialogue response generation. The quality and quantity of available contradictory response data play a vital role in suppressing these contradictions, offering two significant benefits. First, having access to large contradiction data enables a comprehensive examination of their characteristics. Second, data-driven methods to mitigate contradictions may be enhanced with large-scale contradiction data for training. Nevertheless, no attempt has been made to build an extensive collection of model-generated contradictory responses. In this paper, we build a large dataset of response generation models’ contradictions for the first time. Then, we acquire valuable insights into the characteristics of model-generated contradictions through an extensive analysis of the collected responses. Lastly, we also demonstrate how this dataset substantially enhances the performance of data-driven contradiction suppression methods.

## introduction recent large-scale neural response generation models (rgms) have made significant progress @xcite @xcite . however, they still struggle to generate semantically appropriate responses @xcite . among various issues, contradictory responses 1 pose a particularly grave concern. for example, in a conversation between speakers a and b, imagine that in response to speaker a's initial statement, i like tennis, speaker b asks, how often do you play tennis? if speaker a then replies, i hardly ever play. i don't like tennis, this response would be inconsistent with speaker a's initial statement. since these contradictions disrupt the dialogue flow and create a detrimental perception of 1 note that we focus on the contradictions against what is stated in the local context rather than those against the facts in the world. more details are described in appendix a. the rgm's lack of comprehension of the dialogue content @xcite , addressing them is crucial in developing rgms to establish a trustworthy and symbiotic relationship with users, even if they are relatively infrequent. the persistence of contradictory responses in advanced models like chatgpt foot_0 (appendix f) suggests that the problem is not merely a matter of scaling but requires targeted efforts. given this background, several works have proposed approaches for mitigating contradictions (section 2), but the problem remains unresolved and demands further improvements. a significant obstacle hindering further progress in suppressing contradictions is the lack of a largescale collection of rgm-generated contradictory responses. this deficiency poses two problems. first, studies to understand the nature of rgmgenerated contradictions may be impeded. for instance, investigating the correlation between the presence of a certain feature (e.g., a specific dialogue act label) in a dialogue context and the occurrence of an rgm contradiction can aid in developing more effective strategies for mitigating contradictions. regrettably, the available resources are limited to @xcite 's small collection (a few hundred) of rgm-generated contradictory instances intended as test data, making it insufficient for such investigation. second, the efficacy of data-driven contradiction suppression may be limited by a scarcity of training data. as evidenced in various nlp tasks @xcite , the performance of data-driven systems is dependent on the volume of available data. therefore, the efficacy of data-driven contradiction suppression could be improved with access to large-scale rgm contradiction data. although data-driven approaches have been discussed in previous studies (section 2.1), they were based on alternative resources such as automatically synthesized contradictions or humanwritten contradictions @xcite . however, contradictions generated by automatic synthesis or manually are different in characteristics from those actually generated by rgms (section 4.1). if one tries to handle rgms' contradictions with models trained on alternative resources, the potential of data-driven methods may not be fully realized because of the discrepancy between the training data and the practical inference targets, as demonstrated in section 5. in this paper, we demonstrate the effectiveness of having a vast repository of rgm-generated contradictory responses in tackling rgm contradictions. to begin with, we build a large-scale dataset comprising 10k contradictory and 17k noncontradictory responses generated by various highperformance rgms. the consistency of each response is judged by three human annotators, as illustrated in ## dataset construction this paper showcases the importance of employing extensive datasets containing rgm-generated contradictory and noncontradictory responses to mitigate rgm contradictions effectively. as stated earlier, large-scale data are currently lacking. to address this issue, we first perform an extensive collection of rgm-generated instances. this section outlines the methodology used to build our dataset, followed by the detailed settings and the data collection results for this study. ## dataset analysis analyzing the characteristics of rgm contradictions is crucial in devising innovative approaches to mitigate contradictions. the examinations performed by prior studies have been limited by the lack of extensive data, a gap that our dataset effectively fills. sections 4.1 and 4.2 delineate the results of analyzing these characteristics using our data set. section 4.1 delves into the intrinsic features of the generated responses, while section 4.2 presents an examination centered around the dialogue contexts that trigger rgm contradictions. ## experiments this section presents compelling evidence to support the hypothesis that employing the rgmgenerated contradiction collection as a training resource yields notable enhancements in the effectiveness of data-driven contradiction suppression methods. as a case in point, we focus on developing a contradiction detector that automatically classifies whether or not a given utterance pair is contradictory by training it on our dataset. contradiction detectors are commonly employed in post-processing tasks that filter out rgms' contradictory response candidates @xcite and automatic evaluations of rgms' contradiction frequencies @xcite , effectively playing a crucial role in mitigating contradictions. existing detectors have been developed by employing automatically synthesized or humanwritten contradictions as substituting training resources for rgm contradictions. we hypothesize that their performance can be enhanced by utilizing rgm contradiction data for their training. our experiments validate the potency of our dataset by assessing the contradiction detection performance of a detector trained on our dataset against that of a detector trained with human-written contradictions. ## conclusion no attempt has been made to build an extensive collection of rgm-generated contradictory responses, which is problematic in two aspects: the scarcity of data for analysis and training. in this paper, we built a large collection of contradictions generated by various rgms for the first time. we comprehensively analyzed our collection, producing valuable insights into the rgm contradictions, which we believe are crucial for effective contradiction suppression. we also demonstrated that a contradiction detector trained on our dataset could identify rgm contradictions effectively. future challenges include applying the collected dataset to other data-driven methods and collecting data with a broader context variety than fqs.