Counterfactual Matters: Intrinsic Probing For Dialogue State Tracking (2021)

Abstract: A Dialogue State Tracker (DST) is a core component of modular task-oriented dialogue systems. Tremendous research progress has been made in past ten years to improve performance of DSTs especially on benchmark datasets. However, their generalization to novel and realistic scenarios beyond the held-out conversations is limited. In this paper, we design experimental studies to answer: 1) How does the distribution of dialogue data affect the performance of DSTs? 2) What are effective ways to probe counterfactual matter for DSTs? Our findings are: the performance variance of generative DSTs is not only due to the model structure itself, but can be attributed to the distribution of cross-domain values. Evaluating iconic generative DST models on MultiWOZ dataset with counterfactuals results in a significant performance drop of up to 34.64% (from 50.91% to 16.27%) in absolute joint goal accuracy. It is believed that our experimental results can guide the future work to better understand the intrinsic core of DST and rethink the suitable way for specific tasks given the application property.

## introduction a dialogue state tracker (dst) is a pillar of today's task-oriented dialogue systems, which maintains user's intentional goals through the course of a dialogue. in recent years, the creation of large-scale datasets, such as multiwoz @xcite , has fueled the advance of dst models, pushing the accuracy of dst from 15.8%, baseline from @xcite to above 50% @xcite @xcite @xcite @xcite . the common belief is that the more abundant the labeled data, the higher the likelihood of learning diverse phenomena, which in turn leads to models that generalize well. in practice, however, generalization remains as a huge challenge @xcite . motivated by this phenomenon, we aim to address and provide insights into the following question: how well do dst models generalize to the novel but realistic scenarios that are not captured well enough by the held-out evaluation set? answering this question may take us a step closer to bridging the gap between dataset collection and broader task objectives @xcite . most prior work @xcite focus on adversarial example generation for robustness evaluation. they rely on perturbations made directly on test examples in the held-out set and assume direct access to evaluated models' gradients or outputs, which often leads to unnatural examples or hurt target models deliberately. our studies in this paper are not this line of research. recently, the generation-model based approaches for dst instead of a close-set classification approach have attracted more attention. wu proposed a transferable dialogue state generator (trade) @xcite that generates dialogue states from utterances using a copy mechanism, facilitating knowledge transfer between domains. the prominent difference from previous one-domain dst models is that trade is based on a generation approach instead of a close-set classification approach. huang proposed a meta-reinforced multi-domain state generator (meret) @xcite which introduces an end-to-end generative framework with pretrained language model and copy-mechanism, using rl-based generator to encourage higher semantic relevance in greater exploration space for dst. meret holds the similar underlying architecture with trade. quan released modeling long context for task-oriented dialogue state generation (lcdsg) @xcite , which is a multi-task learning model with a simple yet effective utterance tagging technique and a bidirectional language model as an auxiliary task for taskoriented dialogue state generation. lcdsg follows the similar overall framework with trade, too. we conduct our studies in this paper on top of these models trade, meret and lcdsg: first, we propose a simple and efficient counterfactualmaker policy as a principled approach to generate novel scenarios; then, we take a closer look at data sets, and conduct a deep qualitative analysis on data distribution and model structure impact for the dsts. the main contributions of this paper are two-fold: ## proposed approach: svs let us define @xmath0, r 1 ), ..., (u t , r t )} as the set of user utterance and system response pairs in t turns of a dialogue, and @xmath1, ..., b t } as the dialogue state for each turn. dialogue state is represented as slot-value pairs, denoted as b @xmath2, v 1 ), ..., (s j , v j )} where s j and v j (1 ≤ @xmath3. we propose a simple counterfactual-maker approach, slot value substitution (svs). it is used to generate counterfactual dialogue d and corresponding dialogue state b . parameter m is used to represent the ratio of svs. for each dialogue, m percent slot values in b t are selected to be substituted. specifically, for each slot value v j , if the value does not appear in dialogue history, we keep it as it is. for values that can be substituted, new values are sampled from ontology, a predefined value set for each domain-slot. then dialogue history is updated by these new values and counterfactual dialogue d is generated. for state of each previous turn, b t (1 ≤ @xmath4. we get @xmath5, v 1 ), ..., (s j , v j )} for d after the update and we do post-processing human validation on the counterfactuals generated by svs to ensure quality. an example of svs process is shown in figure 1 . ## experiments and analysis in this section, we first describe our observations and concerns from the experiments and then investigate the reason behind. to evaluate the dsts' performance on counterfactual dialogue data, we train dst models following their publicly released implementations on the standard train/dev/test split of multiwoz foot_0 from scratch. joint goal accuracy is used to be the evaluation metric. it measures the accuracy of model prediction at each dialogue turn, and the output is considered correct if and only if all the predicted values exactly match the ground truth values. ## conclusions this paper analyzes reasons leading to performance degradation of generative dsts across controllable counterfactuals. we propose a simple and efficient counterfactual-maker policy as a principled approach to generate novel scenarios beyond the held-out conversations. we find that performance degradation of dsts comes from the ood of counterfactuals and generative composition. these findings are confirmed through experiments on behavior and structure probing, with similar trends. this is of practical interest for applications of dst models, with respect to unlock a true potential of generalization capability.