Diversifying Dialogue Generation with Non-Conversational Text (2020)

Abstract: Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low-diversity problem when it comes to open-domain dialogue generation. As bland and generic utterances usually dominate the frequency distribution in our daily chitchat, avoiding them to generate more interesting responses requires complex data filtering, sampling techniques or modifying the training objective. In this paper, we propose a new perspective to diversify dialogue generation by leveraging non-conversational text. Compared with bilateral conversations, non-conversational text are easier to obtain, more diverse and cover a much broader range of topics. We collect a large-scale non-conversational corpus from multi sources including forum comments, idioms and book snippets. We further present a training paradigm to effectively incorporate these text via iterative back translation. The resulting model is tested on two conversational datasets from different domains and is shown to produce significantly more diverse responses without sacrificing the relevance with context.

## introduction seq2seq models have achieved impressive success in a wide range of text generation tasks. in opendomain chitchat, however, people have found the model tends to strongly favor short, generic responses like "i don't know" or "ok" @xcite . the reason lies in the extreme one-to-many mapping relation between every context and its potential responses @xcite . generic utterances, which can be in theory paired with most context, usually dominate the frequency distribution in the dialogue training corpus and thereby pushes the model to ## related work the tendency to produce generic responses has been a long-standing problem in seq2seq-based open-domain dialogue generation @xcite . previous approaches to alleviate this issue can be grouped into two classes. the first class resorts to modifying the seq2seq architecture itself. for example, @xcite ; @xcite changes the train-ing objective to mutual information maximization and rely on continuous approximations or policy gradient to circumvent the non-differentiable issue for text. @xcite al. (2016d); serban et al. (2017a) treat open-domain chitchat as a reinforcement learning problem and manually define some rewards to encourage long-term conversations. there is also research that utilizes latent variable sampling @xcite @xcite , adversarial learning @xcite , replaces the beam search decoding with a more diverse sampling strategy @xcite or applies reranking to filter generic responses @xcite . all of the above are still trained on the original dialogue corpus and thereby cannot generate out-of-scope topics. the second class seeks to bring in extra information into existing corpus like structured knowledge @xcite @xcite , personal information @xcite or emotions @xcite . however, corpus with such annotations can be extremely costly to obtain and is usually limited to a specific domain with small data size. some recent research started to do dialogue style transfer based on personal speeches or tv scripts @xcite @xcite . our motivation differs from them in that we aim at enriching general dialogue generation with abundant non-conversational text instead of being constrained on one specific type of style. back translation is widely used in unsupervised machine translation @xcite @xcite and has been recently extended to similar areas like style transfer @xcite , summarization @xcite and data-to-text @xcite . to the best of our knowledge, it has never been applied to dialogue generation yet. our work treats the context and non-conversational text as unpaired source-target data. the backtranslation idea is naturally adopted to learn the mapping between them. the contents of nonconversational text can then be effectively utilized to enrich the dialogue generation. ## dataset we would like to collect non-conversational utterances that stay close with daily-life topics and can be potentially used to augment the response space. the utterance should be neither too long nor too short, similar with our daily chitchats. therefore, we collect data from the following three sources: we further filter out sentences with offensive or discriminative languages by phrase matching against a large blocklist. the resulting corpus contains over 1m utterances. the statistics from each source are listed in the context and y i is the corresponding response. d @xmath0, t 2 , . . . , t m } denotes our collected corpus where t i is a non-conversational utterance. as the standard seq2seq model trained only on d tends to generate over-generic responses, our purpose is to diversify the generated responses by leveraging the non-conversational corpus d t , which are semantically and syntactically much richer than responses contained in d. in the following section, we first go through several baseline systems, then introduce our proposed method based on back translation. ## results as for the experiment results, we report the automatic and human evaluation in §6.1 and §6.2 respectively. detailed analysis are shown in §6.3 to elaborate the differences among model performances and some case studies. ## conclusion and future work we propose a novel way of diversifying dialogue generation by leveraging non-conversational text. to do so, we collect a large-scale corpus from forum comments, idioms and book snippets. by training the model through iterative back translation, it is able to significantly improve the diversity of generated responses both semantically and syntactically. we compare it with several strong baselines and find it achieved the best overall performance. the model can be potentially improved by filtering the corpus according to different domains, or augmenting with a retrieve-and-rewrite mechanism, which we leave for future work.