CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers

1 INTRODUCTION . Task-oriented dialogue ( TOD ) systems have recently attracted growing attention and achieved substantial progress ( Zhang et al. , 2019b ; Neelakantan et al. , 2019 ; Peng et al. , 2020 ; Wang et al. , 2020b ; a ) , partly made possible by the construction of large-scale datasets ( Budzianowski et al. , 2018 ; Byrne et al. , 2019 ; Rastogi et al. , 2019 ) . Dialogue state tracking ( DST ) is a backbone of TOD systems , where it is responsible for extracting the user ’ s goal represented as a set of slot-value pairs ( e.g. , ( area , center ) , ( food , British ) ) , as illustrated in the upper part of Figure 1 . The DST module ’ s output is treated as the summary of the user ’ s goal so far in the dialogue and directly consumed by the subsequent dialogue policy component to determine the system ’ s next action and response . Hence , the accuracy of the DST module is critical to prevent downstream error propagation ( Liu and Lane , 2018 ) , affecting the end-to-end performance of the whole system . With the advent of representation learning in NLP ( Pennington et al. , 2014 ; Devlin et al. , 2019 ; Radford et al. , 2019 ) , the accuracy of DST models has increased from 15.8 % ( in 2018 ) to 55.7 % ( in 2020 ) . While measuring the held-out accuracy is often useful , practitioners consistently overestimate their model ’ s generalization ( Ribeiro et al. , 2020 ; Patel et al. , 2008 ) since test data is usually collected in the same way as training data . In line with this hypothesis , Table 1 demonstrates that there is a substantial overlap of the slot values between training and evaluation sets of the MultiWOZ DST benchmark ( Budzianowski et al. , 2018 ) . In Table 2 , we observe that the slot co-occurrence distributions for evaluation sets tightly align with that of train split , hinting towards the potential ∗Equal Contribution . Work was done during Shiyang ’ s internship at Salesforce Research . 1Code is available at https : //github.com/salesforce/coco-dst limitation of the held-out accuracy in reflecting the actual generalization capability of DST models . Inspired by this phenomenon , we aim to address and provide insights into the following question : how well do state-of-the-art DST models generalize to the novel but realistic scenarios that are not captured well enough by the held-out evaluation set ? Most prior work ( Iyyer et al. , 2018 ; Jin et al. , 2019 ) focus on adversarial example generation for robustness evaluation . They often rely on perturbations made directly on test examples in the heldout set and assume direct access to evaluated models ’ gradients or outputs . Adversarial examples generated by these methods are often unnatural or obtained to hurt target models deliberately . It is imperative to emphasize here that both our primary goal and approach significantly differ from the previous line of work : ( i ) Our goal is to evaluate DST models beyond held-out accuracy , ( ii ) We leverage turn-level structured meaning representation ( belief state ) along with its dialogue history as conditions to generate user response without relying on the original user utterance , ( iii ) Our approach is entirely model-agnostic , assuming no access to evaluated DST models , ( iv ) Perhaps most importantly , we aim to produce novel but realistic and meaningful conversation scenarios rather than intentionally adversarial ones . We propose controllable counterfactuals ( COCO ) as a principled , model-agnostic approach to generate novel scenarios beyond the held-out conversations . Our approach is inspired by the combination of two natural questions : how would DST systems react to ( 1 ) unseen slot values and ( 2 ) rare but realistic slot combinations ? COCO first encapsulates these two aspects under a unified concept called counterfactual goal obtained by a stochastic policy of dropping and adding slots to the original turnlevel belief state followed by replacing slot values . In the second step , COCO conditions on the dialogue history and the counterfactual goal to generate counterfactual conversation . We cast the actual utterance generation as a conditional language modeling objective . This formulation allows us to plug-in a pretrained encoder-decoder architecture ( Raffel et al. , 2020 ) as the backbone that powers the counterfactual conversation generation . We also propose a strategy to filter utterances that fail to reflect the counterfactual goal exactly . We consider value substitution ( VS ) , as presented in Figure 1 , as a special COCO case that only replaces the slot values in the original utterance without adding or dropping slots . When we use VS as a fall-back strategy for COCO ( i.e. , apply VS when COCO fails to generate valid user responses after filtering ) , we call it COCO+ . Evaluating three strong DST models ( Wu et al. , 2019 ; Heck et al. , 2020 ; Hosseini-Asl et al. , 2020 ) with our proposed controllable counterfactuals generated by COCO and COCO+ shows that the performance of each significantly drops ( up to 30.8 % ) compared to their joint goal accuracy on the original MultiWOZ held-out evaluation set . On the other hand , we find that these models are , in fact , quite robust to paraphrasing with back-translation , where their performance only drops up to 2 % . Analyzing the effect of data augmentation with COCO+ shows that it consistently improves the robustness of the investigated DST models on counterfactual conversations generated by each of VS , COCO and COCO+ . More interestingly , the same data augmentation strategy improves the joint goal accuracy of the best of these strong DST models by 1.3 % on the original MultiWOZ evaluation set . Human evaluations show that COCO-generated counterfactual conversations perfectly reflect the underlying user goal with more than 95 % accuracy and are found to be quite close to original conversations in terms of their human-like scoring . This further proves our proposed approach ’ s reliability and potential to be adopted as part of DST models ’ robustness evaluation . 2 RELATED WORK . Dialogue State Tracking . DST has been a core component in current state-of-the-art TOD systems . Traditional approaches usually rely on hand-crafted features or domain-specific lexicon ( Henderson et al. , 2014 ; Wen et al. , 2017 ) and require a predefined ontology , making them hard to extend to unseen values . To tackle this issue , various methods have been proposed . Gao et al . ( 2019 ) treats DST as a reading comprehension problem and predicts slot values with start and end positions in the dialogue context . Zhang et al . ( 2019a ) proposes DS-DST , a dual-strategy model that predicts values in domains with a few possible candidates from classifiers and others from span extractors . Furthermore , Heck et al . ( 2020 ) proposes TripPy , a triple copy strategy model , which allows it to copy values from the context , previous turns ’ predictions and system informs . An alternative to classification and span prediction is value generation . Wu et al . ( 2019 ) generates slot values with a pointer generator network See et al . ( 2017 ) without relying on fixed vocabularies and spans . ( Hosseini-Asl et al. , 2020 ) models DST as a conditional generation problem and directly finetunes GPT2 ( Radford et al. , 2019 ) on DST task and achieves state-of-the-art on the MultiWOZ . Adversarial Example Generation . Adversarial example generation has been commonly studied in computer vision ( Szegedy et al. , 2014 ; Goodfellow et al. , 2015 ) . Recently , it has received growing attention in NLP domain as well . Papernot et al . ( 2016 ) finds adversarial examples in the embedding space , and then remapped them to the discrete space . Alzantot et al . ( 2018 ) proposes a populationbased word replacing method and aims to generate fluent adversarial sentences . These methods often edit the original data greedily assuming access to the model ’ s gradients or outputs besides querying the underlying model many times ( Jin et al. , 2019 ) . Alternative line of work investigates generating adversarial examples in a model-agnostic way . Iyyer et al . ( 2018 ) proposes to generate adversarial paraphrases of original data with different syntactic structures . Jia and Liang ( 2017 ) automatically generates sentences with key word overlappings of questions in SQuAD ( Rajpurkar et al. , 2016 ) to distract computer systems without changing the correct answer or misleading humans . Although different methods have been proposed to evaluate the robustness of NLP models , majority of the prior work in this line focus either on text classification , neural machine translation or reading comprehension problems . Perhaps the most similar existing work with ours are ( Einolghozati et al. , 2019 ) and ( Cheng et al. , 2019 ) . Einolghozati et al . ( 2019 ) focuses on intent classification and slot tagging in TOD while Cheng et al . ( 2019 ) targets at synthetic competitive negotiation dialogues ( Lewis et al. , 2017 ) without DST component . In this work , however , we focus on evaluating a core component of state-of-the-art TOD , DST , on the widely used benchmark , MultiWOZ . To the best of our knowledge , ours is the first work to systematically evaluate the robustness of DST models . 3 BACKGROUND . Multi-domain DST task definition . Let Xt = { ( U sys1 , U usr1 ) , ... , ( U sys t , U usr t ) } denote a sequence of turns of a dialogue until the t-th turn , where U sysi and U usr i ( 1 ≤ i ≤ t ) denote system and user utterance at the i-th turn , respectively . In multi-domain DST , each turn ( U sysi , U usr i ) talks about a specific domain ( e.g. , hotel ) , and a certain number of slots ( e.g. , price range ) in that domain . We denote all N possible domain-slot pairs as S = { S1 , ... SN } . The task is to track the value for each Sj ( 1 ≤ j ≤ N ) over Xt ( e.g. , hotel-price range , cheap ) . Belief states can be considered at two granularities : turn-level ( Lt ) and dialog-level ( Bt ) . Lt tracks the information introduced in the last turn while Bt tracks the accumulated state from the first turn to the last . As illustrated in the upper part of Figure 1 , when the dialogue flow arrives at the second turn , B2 becomes { ( restaurant-area , center ) , ( restaurant-food , British ) , ( restaurant-book time , 18:00 ) } , while L2 is { ( restaurant-food , British ) , ( restaurant-book time , 18:00 ) } , essentially tracking the update to Bt by the last turn . Problem definition . Given a tuple < Xt , Lt , Bt > , our goal is to generate a new user utterance Û usrt to form a novel conversation scenario X̂t = { ( U sys1 , U usr1 ) , ... , ( U sys t , Û usr t ) } by replacing the original user utterance U usrt with Û usr t . To preserve the coherence of dialogue flow , we cast the problem as generating an alternative user utterance Û usrt conditioned on a modified L̂t derived from original turn-level belief state Lt in a way that is consistent with the global belief state Bt . This formulation naturally allows for producing a new tuple < X̂t , L̂t , B̂t > controllable by L̂t , where B̂t is induced byBt based on the difference between Lt and L̂t . As illustrated in the lower part of Figure 1 , U usr2 is replaced with the two alternative utterances that are natural and coherent with the dialogue history . We propose to use the resulting set of < X̂t , L̂t , B̂t > to probe the DST models . Paraphrase baseline with back-translation . Paraphrasing the original utterance U usrt is a natural way to generate Û usrt . With the availability of advanced neural machine translation ( NMT ) models , round-trip translation between two languages ( i.e. , back-translation ( BT ) ) has become a widely used method to obtain paraphrases for downstream applications ( Yu et al. , 2018 ) . We use publicly available pretrained English→German ( log ( g|e ) ) and German→English ( log ( e|g ) ) NMT models.2 We translate U usrt from English to German with a beam size K , and then translate each of the K hypotheses back to English with the beam size K. Consequently , we generate K2 paraphrase candidates of Û usrt and then rank them according to their round-trip confidence score log ( g|e ) + log ( e|g ) . As paraphrases are expected to preserve the meaning of U usrt , we set L̂t = Lt and B̂t = Bt .

Summary: This paper presents an interesting approach to generate dialogs in a controllable fashion to evaluate a Dialog State Tracking system on a data distribution which is different from the training/test data. The proposed approach first generates a turn-level goal by adding or dropping a slot and then replacing slot values. In the second step, the proposed method generates counterfactual conversation conditioned on the dialog history and goal generated in the previous step. The authors show that evaluating current state-of-the-art DST model on MultiWOZ datasets with the generated counterfactuals results in significant performance drop. Additionally, human evaluation shows that the generated conversations perfectly reflect the underlying user goal.