D ialo GPS : Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations (2023)

Abstract: In open-domain dialogue generation tasks, contexts and responses in most datasets are one-to-one mapped, violating an important many-to-many characteristic: a context leads to various responses, and a response answers multiple contexts. Without such patterns, models poorly generalize and prefer responding safely. Many attempts have been made in either multi-turn settings from a one-to-many perspective or in a many-to-many perspective but limited to single-turn settings. The major challenge to many-to-many augment multi-turn dialogues is that discretely replacing each turn with semantic similarity breaks fragile context coherence. In this paper, we propose DialoGue Path Sampling (DialoGPS) method in continuous semantic space, the first many-to-many augmentation method for multi-turn dialogues. Specifically, we map a dialogue to our extended Brownian Bridge, a special Gaussian process. We sample latent variables to form coherent dialogue paths in the continuous space. A dialogue path corresponds to a new multi-turn dialogue and is used as augmented training data. We show the effect of DialoGPS with both automatic and human evaluation.

## introduction open-domain dialogue generation has received significant attention and has made notable advancements @xcite @xcite . however, it still faces challenges due to the nature of the data. one specific challenge is the many-to-many relationship between contexts and responses in open-domain conversations. a context can lead to various responses, and a response can be relevant to multiple contexts. unfortunately, most datasets only provide one-to-one a: a man came into the bank with a gun. b: that's insane! a: let me tell you what happened. ## related work: dialogue generation augmentation in general, dialogue generation can be categorized into two groups: task-oriented and open-domain. open-domain generation is a context-aware process that lasts for turns. the model learns to generate a proper but open response from the preceding utterances (i.e., contexts). task-oriented dialogues progress for specific purposes and are limited to specific domains, such as obtaining knowledge @xcite . however, due to the specific domains in task-oriented dialogues, the many-to-many relationship is not as apparent compared to open-domain dialogues. in this paper, we focus on open-domain dialogue generation augmentation from an x-to-many perspective. from a one-to-many perspective, sai et al. (2020) manually denoted multiple responses for a dialogue context. based on such multi-reference datasets, @xcite proposed to capture the common feature in feasible responses and then add the specific feature to obtain the final output, which augments the utility of the data and improves the generalization. @xcite proposed that with only one-to-one data, models can construct pseudotarget data in the decoder and improve the model by bootstrapping. from a many-to-many perspective, existing methods work in single-turn settings. @xcite generated multiple context or responses with cvae @xcite and introduced a gan @xcite discriminator to filter incoherent sentence pairs. zhang et al. (2020a) augmented a one-to-one dialogue dataset d p with an unpaired sentence set d u . they sample sentences from d u and replace the most similar sentences in d p . they use bert @xcite and knowledge distillation to filter noise in incoherent sentence pairs. until now, manyto-many augmentation in multi-turn settings are understudied. ## method we first present some preliminaries ( § 3.1). then, we introduce mapping dialogue texts to the desired latent space ( § 3.2), augmented data construction ( § 3.3), augmented data utilization ( § 3.4), and inference details ( § 3.5). figure 2 shows the overview of dialogps. ## experimental settings datasets we conduct multi-turn dialogue generation experiments on two public datasets: daily-dialog @xcite and personachat @xcite . dailydialog contains high-quality multi-turn dialogues collected from daily conversations, and it has many multi-reference versions @xcite denoted by humans, which makes it possible for us to compare dialogps with human annotators. besides, it is more reliable to evaluate the generalization and performance with multiple references. personachat collects dialogues based on chatters' profiles. profiles are not shown to models, so it is more challenging and open to generate proper responses, measuring generalization capacity better. baselines and parameters we compare di-alogps with (1) transformer @xcite . because dialogps is a plug-and-play method, we add it to a bart large @xcite and compare with dialoflow large @xcite . di-aloflow is one of the state-of-the-art pre-trained models in open-domain dialogue generation. it augments the model by modeling the dialogue flow. more details on the implementation and hyperparameters are in appendix a.1. evaluation metrics we consider three automatic evaluation metrics: bleu @xcite , distinct (dist) @xcite , and bleurt @xcite . bleu measures the word overlap between generated responses and the ground truth. dist measures the ratio of unique n-grams in the generated responses. because these two metrics are only sensitive to lexical variation, we evaluate bleurt, an advanced learned semanticsensitive evaluation metric based on bert @xcite . on the evaluation of fine-tuning pre-trained models, we follow @xcite to report meteor @xcite when there is no mixup in either encoder or decoder, the model degenerates into a vanilla transformer. entropy @xcite . for human evaluation, we recruit five evaluators to manually judge 200 samples from each experiment in blind testing, where we set three metrics to comprehensively evaluate the generation quality: whether a response is readable (read.), coherent (coh.), and informative (info.). for each aspect, evaluators can score at 'bad', 'borderline' and 'good'. ## results ## conclusion we propose dialogps that first augments opendomain and multi-turn dialogue generation from a many-to-many perspective. specifically, we map dialogues into the continuous semantic space which is modeled by our extended brownian bridge and sample dialogue paths to augment training. we propose a self-distillation framework to utilize augmented data despite the inaccessible discrete labels. empirically, we prove the effect of dialogps and study its characteristics. dialogps could be a general method that suits seq2seq tasks where the source has multiple sentences and the target is different from the source in semantics, like summarization. however, dialogps should be modified according to the unique properties of the task, which is left to study in the future.