Modeling Complex Dialogue Mappings via Sentence Semantic Segmentation Guided Conditional Variational Auto-Encoder (2022)

Abstract: Complex dialogue mappings (CDM), including one-to-many and many-to-one mappings, tend to make dialogue models generate incoherent or dull responses, and modeling these mappings remains a huge challenge for neural dialogue systems. To alleviate these problems, methods like introducing external information, reconstructing the optimization function, and manipulating data samples are proposed, while they primarily focus on avoiding training with CDM, inevitably weakening the model’s ability of understanding CDM in human conversations and limiting further improvements in model performance. This paper proposes a Sentence Semantic Segmentation guided Conditional Variational Auto-Encoder (SegCVAE) method which can model and take advantages of the CDM data. Specifically, to tackle the incoherent problem caused by one-to-many, SegCVAE uses response-related prominent semantics to constrained the latent variable. To mitigate the non-diverse problem brought by many-to-one, SegCVAE segments multiple prominent semantics to enrich the latent variables. Three novel components, Internal Separation, External Guidance, and Semantic Norms, are proposed to achieve SegCVAE. On dialogue generation tasks, both the automatic and human evaluation results show that SegCVAE achieves new state-of-the-art performance.

## introduction the ratio of samples with cdm on emb.aver. and coherence are small. this result proves the idea that reducing the cdm of the dataset is beneficial for increasing the scores of some automatic evaluation metrics. however, these methods simply ignore the cdm data (10% of the dataset), and in this paper, we argue that these cdm dialogue pairs are still valuable for dialogue training. to explore this, we conduct further experimental investigation by training two sequence-to-sequence dialogue models (seq2seq) @xcite over the "clean" opensubtitles dataset which does not contain any one-to-many or many-to-one pairs, respectively, and then we gradually introduce one-tomany/many-to-one pairs to fine-tune these models. from figure 1 , we observe that one-to-many and many-to-one dialogue pairs have conflicting effects on distinct, emb.aver. and coherence, which explains why simply removing them together yields smaller gains. therefore, instead of staying away from cdm, our primary study of interest is to enable model to effectively learn useful knowledge from these dialogue pairs while avoiding being affected by the disadvantages. to achieve this goal, we take inspirations from conditional variational autoencoder (cvae) based dialogue generation methods @xcite @xcite @xcite and model the manyto-one and one-to-many from the latent space. however, previous study shows that due to lack of the prior knowledge, latent variable hardly involves semantic relationships, resulting in semantically irrelevant responses @xcite . therefore, we propose a sentence semantic segmentation guided good morning, do you want coffee or tea? good morning, have a nice day. what would you want, coffee or tea? good morning, may i help you? bring me a cup of coffee, please. i do not know. good morning, do you want coffee or tea? good morning, have a nice day. bring me a cup of coffee, please. semantic segmentation what would you want, coffee or tea? good morning, may i help you? good morning, have a nice day. bring me a cup of coffee, please. i do not know. ## related work the open-domain dialogue generation has received dramatic attention recently @xcite @xcite . @xcite identified that "noisy" data, including one-to-many and many-to-one dialogue pairs, can affect the performance of dialogue systems. to address such "noisy" data, many methods have been proposed in recent years. for instance, a large body of work on introducing external information for reducing the number of noisy data @xcite @xcite @xcite @xcite @xcite , and a rich line of work reconstructs the objective function to avoid training models directly on such noisy data @xcite @xcite @xcite @xcite @xcite @xcite . others design a scoring approach to filter noisy data @xcite @xcite ; however, cdm data in human conversations impels valuable information that can help models generate better responses, and these methods cannot learn the valuable information of one-to-many and many-to-one dialogue pairs, nor can they make full use of the advantages of these data. for example, @xcite uses personal information to reduce the one-to-many dialogue pairs. the reinforcement learning based dialogue generation methods @xcite only require the generated response to get high reward rather than similar with the ground-truth, which means that some many-to-one dialogue pairs are ignored during training. @xcite uses conditional entropy to assess the dialogue pairs, which easily filters one-to-many and many-to-one dialogue pairs. in addition to the methods above, cvae-based dialogue generation methods @xcite @xcite @xcite provide an idea to learn the essential knowledge of the oneto-many and many-to-one mappings. they try to encode knowledge into a latent space, a posterior probability distribution, and a prior probability distribution. by sampling latent variables, the model can easily generate multiple responses for one context. we follow this rich line of work to explore their applicability in modeling cdm, and we propose new state-of-the-art segcvae in dialogue generation task. compared with the vanilla cvae, segcvae uses sentence semantic segmentation to regularize and guide the latent variables, which avoids the gap between context and latent variables. different from knowledge-guide cvae, segcvae does not require additional information. meanwhile, segcvae uses the segmented prominent semantics instead of manually-created orthogonal vectors, which is more reasonable than sepacvae. ## segcvae segcvae is proposed to model cdm (including one-to-many and many-to-one mappings) through sentence semantic segmentation guided latent variables. as discussed above, different prominent semantics can be segmented from one context semantics, and similar prominent semantics can be segmented from different context semantics, which help latent variables learn the semantic relations, thus modeling one-to-many and many-to-one naturally. in this section, we provide detailed descriptions of the proposed segcvae method. ## conclusion this paper proposes a novel segcvae to model complex dialogue mappings (cdm) in human conversations. segcvae parses the cdm from a semantic perspective: using multiple prominent semantics segmented from the context to establish relationships with the responses, multiple prominent semantics can correspond to multiple responses, and multiple contexts can also segment similar prominent semantics. in this way, prominent semantics can constrain latent variables to learn semantic relations to tackle incoherent problem, while enriching them to mitigate the non-diverse problem. to realize segcvae, we propose three novel modules: internal separation (is), external guidance (eg), and semantic norms (i.e. l san , l scn , and l sdn ). is is used to get the basic information for computing prominent semantics, eg is used to constrain the prominent semantics not to deviate too far from the original semantics, and three semantic norms are proposed to establish relationships for contexts, prominent semantics and responses. the experimental results show the superiority of our model in dialogue generation, one-tomany and many-to-one dialogue learning tasks.