I already said that! Degenerating redundant questions in open-domain dialogue systems. (2023)

Abstract: Neural text generation models have achieved remarkable success in carrying on short open-domain conversations. However, their performance degrades significantly in the long term, especially in their ability to ask coherent questions. A significant issue is the generation of redundant questions where the answer has already been provided by the user. We adapt and evaluate different methods, including negative training, decoding, and classification, to mitigate the redundancy problem. We also propose a simple yet effective method for generating training data without the need for crowdsourcing human-human or human-bot conversations. Experiments with the BlenderBot model show that our combined method significantly reduces the rate of redundant questions from 27.2% to 8.7%, while improving the quality of the original model. The code, dataset, and trained models can be found at our repository.

## introduction despite recent significant improvements in text generation techniques, open-domain dialogue generation is nowhere near perfect. large-scale neuralbased models, such as gpt-3 @xcite and blenderbot @xcite @xcite , still present many issues including but not limited to contradiction @xcite , "hallucinations" @xcite , offensive and toxic responses @xcite , which undermine their use in real-world applications. as a result, many social chatbots @xcite still rely heavily on hand-designed dialogue managers and scripted responses. end-to-end neural-based models are only used for handling unexpected inputs, but only for a few turns, before giving back control to the handdesigned dialogue manager @xcite . although neural-based models have shown superior performance in generating statement responses, 1 https://github.com/mailong25/redundancy-dialogue they are also reported to ask undesirable questions such as redundant, irrelevant, and topic-changing questions @xcite . this is because the models are often trained on short conversations, which results in generating questions that prioritize local appropriateness over global cohesiveness. this is why the quality of generated questions often degrades rapidly when the conversation is carried on over multiple turns. to address difficulties of long-term dialogue generation, a multi-session dialogue dataset (msc) @xcite has been proposed with an average conversation turn of 53; this is significantly higher than any of the previous datasets, of 2-15 turns. the authors also proposed a memory-augmented model that makes use of summary of the conversation for generating global-coherent responses. however, the issue of redundant questions is still present. figure 1 shows examples of redundant questions generated by the recent blenderbot 3.0 (bb3) chatbot @xcite , partly trained on msc with memory-augmentation. redundant questions can be categorized into explicit and implicit. explicit are questions that have been asked previously in the dialogue context while implicit are the ones in which the answers are already given or can be inferred but was not previously asked. the problem of redundant questions can also be attributed to the maximum likelihood training objective that does not explicitly teach the model what kinds of questions it should not ask. although several techniques, such as unlikelihood training @xcite , negative training @xcite , and contrastive learning @xcite have been proposed to mitigate undesirable behaviors of maximum likelihood training, none of them have been focused on preventing bad questions from being generated. this study is the first to address the problem of redundant questions in open-domain dialogue systems. we adapt and evaluate different methods, including unlikelihood training, contrastive training, contrastive decoding, and classification to mitigate the redundancy problem. whether a question is redundant or not is determined based on the previous speaker's personas, which are input to the model alongside the truncated dialogue history. as there are no relevant datasets for this task, we created the first one, called the non-redundant questions (nrq) dataset, to facilitate training. to demonstrate the effectiveness of the proposed method, we apply it to improve the question-asking ability of the blenderbot 2.0 model (bb2) @xcite -a simpler version, but comparable to the recent bb3 model. experimental results show that our proposed methods reduce the redundant question rate of the original bb2 model from 27.2% to 8.7%, which results in better overall performance. ## experiment results redundant question classification. we first report performances of our redundant question classifier in conversation length vs redundant rate. as shown in figure 4 , the redundant question rate increases significantly with respect to the length of the conversation. for bb2 baseline, the rate is 18.4% at turn 30. the number further increases by another 8.1% when the conversation reaches 65 turns. however, this issue is not a concern in previous studies as most evaluate the chatbots on a short conversation setting (less than 10 turns). the increase in redundant rate can be attributed to the limited number of topics the chatbot can initiate. when the conversation is prolonged, it often revisit topics that have already been discussed. truncated context length vs redundant rate. the limitation of 128 tokens for truncated dialogue in the bb2 baseline could be the cause of higher redundant question rate. increasing the truncation length could be considered as a possible solution to address this issue. to investigate this hypothesis, we utilized the msc model @xcite , which was specifically trained on the msc dataset to effectively handle long conversations. in figure 4 , the results demonstrate a significant reduction in redundancy rates by extending the truncation length. for conversations with a length of 30, the redundancy rate decreased from 18.4% (truncated at 128) to 10.4% (truncated at 1024). however, it is important to note that despite these improvements, they still fall short compared to the bb2 tuned model using our proposed methods, while also incurring increased training and inference costs. bias in training data. another contributing factor to the redundant issue is the bias of the bb2 baseline towards common topics, such as pets, hobbies, and careers, which increases the likelihood of repeating the same topics over again. an explanation can be seen in ## predictions analysis we present several successful and failed cases of the proposed approach. ## conclusion asking good questions is an important skill for a chatbot to engage in a long-term conversation. this study first introduces the problem of redundant questions in neural text generation models. several methods, including negative training, decoding, and classification have been proposed to lower the probabilities of these undesirable questions. we also create the first-of-its-kind dataset named nrq dataset containing training samples with a redundant question assigned to each dialogue context and speaker personas. we validate our methods with the bb2 model and observed a significant reduction of the redundant rate, which results in a higher rating for the questioning skills of the chatbot. we believe the proposed approaches and datasets will be beneficial for building future dialogue systems.