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 addresses the problem of evaluating dialogue state trackers (DST)’s generalization ability to novel and realistic dialogue scenarios that do not exist in the dataset. It proposes a model-independent approach to evaluate DST systems with the idea of counterfactual conversation generation. The proposed approach is integrated with three recent DST models and evaluated on MultiWoZ dataset.