Sources of Noise in Dialogue and How to Deal with Them (2023)

Abstract: Training dialogue systems often entails dealing with noisy training examples and unexpected user inputs. Despite their prevalence, there currently lacks an accurate survey of dialogue noise, nor is there a clear sense of the impact of each noise type on task performance. This paper addresses this gap by first constructing a taxonomy of noise encountered by dialogue systems. In addition, we run a series of experiments to show how different models behave when subjected to varying levels of noise and types of noise. Our results reveal that models are quite robust to label errors commonly tackled by existing denoising algorithms, but that performance suffers from dialogue-specific noise. Driven by these observations, we design a data cleaning algorithm specialized for conversational settings and apply it as a proof-of-concept for targeted dialogue denoising.

## introduction quality labeled data is a necessity for properly training deep neural networks. more data often leads to better performance, and dialogue tasks are no exception @xcite . however, in the quest for more data, practitioners increasingly rely on crowdsourcing or forms of weak supervision to meet scaling requirements. even when acting in good faith, crowdworkers are not trained experts which understandably leads to mistakes. this ultimately results in noisy training inputs for our conversational agents. moreover, when dialogue systems are deployed into the real world, they must also deal with noisy user inputs. for example, a user might make an ambiguous request or mention an unknown entity. all these sources of noise eventually take their toll on model performance. before building noise-robust dialogue systems or denoising dialogue datasets, it would be helpful to know what types of noise exist in the first place. then our efforts can be spent more wisely tackling the sources of noise that actually make a i'm looking for a chinese restaurant on the south side of town. book me a table for 7 on thursday at 17:30 and send me a reference number. thank you. there are 2 expensive and 1 cheap restaurant matching your criteria. which price range do you prefer? (@xmath0, @xmath1. i will need a reference number please for the reservation. yes please. let's try the other one you mentioned. (@xmath2, @xmath3, @xmath4:30, @xmath5, @xmath6, @xmath7, @xmath8:30, @xmath9, @xmath10, @xmath11, @xmath12:30, @xmath13, @xmath14 let's decide on the lucky star. however, that appears to already be booked. do you want to try one of the others instead? figure 1 : an example of label errors within multiwoz 2.0 which contains partially filled and missing labels. we categorize this as two types of instance-level noise. to investigate these concerns, we survey a wide range of popular dialogue datasets and outline the different types of naturally occurring noise. building on this exercise, we also study the patterns of annotation errors to determine the prevalence of each noise type and identify the most likely causes of noise. next, we run transformer models through the gamut to find out how well they handle the different types of noise documented in the previous step. in total, we test 3 model types on 7 categories in total, our contributions are as follows: (a) construct a realistic taxonomy of dialogue noise to guide future data collection efforts. (b) measure the impact of noise on multiple tasks and neural models to aid the development of denoising algorithms. (c) establish a strong baseline for dealing with noise by resolving dialogue specific concerns, and verify its effectiveness in practice. ## dialogue datasets a data-driven taxonomy of dialogue noise was designed by manually reviewing thousands of conversations across ten diverse datasets and their accompanying annotations. the datasets were chosen from non-overlapping domains to exhaustively represent all commonly considered dialogue tasks. at a high level, they are divided into six task-oriented dialogue datasets and four open domain chit-chat datasets. the task-oriented datasets are comprised of multiwoz 2.0 (mwoz) @xcite , tickettalk (tt) @xcite , schema guided dialogue (sgd) @xcite , action based conversations dataset (abcd) @xcite , google simulated conversations (gsim) @xcite , and key-value retrieval for in-car as-sistant (kvret) @xcite . the open domain datasets include dailydialog (dd) @xcite , wizard of wikipedia (wow) @xcite , empathetic dialogues (ed) @xcite , and machine interaction dialog act schema (midas) @xcite . the datasets also span a variety of data collection methodologies, such as m2m or wizard-of-oz, which has a close connection to the types of noise produced. we also consider whether the interlocutors engage in realtime vs. non-synchronous chat. details of each dataset can be found in the taxonomy creation process starts by uniformly sampling 1% of conversations from each corpus, rounding up as needed to include at least 100 dialogues per dataset. five expert annotators then conducted two rounds of review per conversation to tally noise counts, with a third round to break ties if needed. the group also crossreferenced each other to merge duplicate categories and resolve disagreements. notably, the final taxonomy purposely excludes sources of noise that occur less than 0.1% of the time. this active curation supports future denoising research by focusing attention on the most prominent sources of noise. ## sources of noise through careful review of the data, we discover that dialogue systems encounter issues either from noisy training inputs during model development or from noisy user inputs during model inference. ## noise patterns beyond categorization, manually reviewing 10k+ utterances also provides unique insights. how often does noise appear? the percentage of dialogues with at least one instance of noise comes out to an average of 11.2%, a median of 10.6%, with a standard deviation of 3.7%. however, given the approximate nature of sampling, the extra digits may not be significant. instead, we assert the rate of noise in curated dialogue datasets is usually over 5%, rarely above 20% and typically around 10%. since these rates are relatively low, denoising techniques aiming to combat extremely high levels of noise may be impractical. what noise types are most common? while most existing denoising algorithms are designed to resolve class confusion @xcite @xcite , our analysis reveals that instance-level noise is actually much more common, showing up in nearly 10% of cases compared to just 5% for classlevel errors. class-level noise assumes a latent noise transition matrix stochastically switches labels from one class to another. however, the prevalence of instance-level noise implies that the more likely explanation is that some examples are simply more confusing then others due to the genuinely ambiguous nature of dialogue @xcite from an algorithmic perspective, the upshot is that developing denoising methods to target individual examples rather than class errors are likely to be most effective. furthermore, we discovered that noise is clustered rather than evenly distributed, so filtering out or relabeling these particularly noisy instances should have an out-sized impact. why is x source of noise missing? the expected influence of some sources of noise are greatly exaggerated. building out the taxonomy not only shows the most likely sources of noise, but equally notable is uncovering the least likely noise types. concretely, the threat of adversarial actors is largely overblown @xcite , as spam-like activity appears less than 2% of the time. offensive speech is the subject of numerous dialogue studies @xcite @xcite , but is practically non-existent in reality (<0.5% of cases). while hate speech may be a problem when training on raw web text @xcite , our empirical review reveals that toxic language is exceedingly rare in curated datasets. instead, unnatural utterances generated by crowdworkers role-playing as real users occurs much more often. (full breakdown in appendix e) other types of noise occur so infrequently that they are missing from the taxonomy completely! noteworthy options include inconsistent names or titles within the ontology (see appendix c), as well as improper reference texts for dialogue generation tasks. while these noise types are possible, they did not occur in practice. we intentionally exclude all such candidates from the taxonomy since the aim is not to be comprehensive, but rather to highlight where researchers should spend their efforts. where does noise come from? our survey found that each data collection method had a propensity to produce certain kinds of noise. this suggests noise arises as a result of how examples are annotated, rather than other factors such as conversation length (number of utterances) or dialogue style (open-domain vs. task-oriented). for example, positive discourse attributes are most common with post-conversation annotation and live chat, which involve two human speakers engaging in real dialogue. wizard-of-oz datasets are less timeconsuming to produce, but contain more label noise. in contrast, dialogues from machine-to-machine or dialog self-play (ie. starting with the labels to generate the dialogue) contain fewer label errors, but also sound less natural. separately, annotator and ontology issues can be mitigated with well-written agent guidelines and proactive crowdworker screening. thus, practitioners should consider these noise trade-offs when collecting dialogue data. ## experiments and results this section explores to what degree various models and dialogue tasks are impacted by each of the seven different categories of noise outlined in section 3. to study this, a model is trained on a clean version of the dataset and on a corrupted version with either natural or injected noise. the level of corruption for all trials is held constant at 10% to allow for comparison across noise types. datasets for each noise type are selected to maximize the overall variety, while always keeping one instance of multiwoz 2.3 to aid comparison. intuitively, sources of noise that induce a larger gap in models trained on cleaned versus corrupted data are more significant, and consequently deserve more attention as targets to denoise. ## dialogue denoising informed by our understanding of the sources of dialogue noise, we now design a preliminary denoising algorithm for learning in the presence of noisy labels. we select multiwoz to serve as our testbed not only because it is one of the most popular dialogue datasets, but also because it is representative of how noise affects most datasets in general (see figure 6 ). while our method produces promising results, our aim is not to declare the noise issue solved, but rather to establish a baseline others can further improve. (more details in appendix g.) ## related works our work is related to efforts to categorize noise within speech and dialog. @xcite proposed a theory of miscommunication consisting of channel, signal, intention and conversation where each of the four levels serves as a potential vector for noise. others have also studied noise in spoken dialogue systems, where they found that the main culprit stems from errors in speech transcription @xcite . rather than a high-level framework of general communication, our hierarchical taxonomy focuses on understanding the multiple layers of noise found in written text. more recent works on dialogue noise discuss robustness to noisy user inputs, whereas we expand this view to also analyze noisy training in-puts. @xcite introduce raddle as a platform which covers ood due to paraphrasing, verbosity, simplification, and unseen entities, as well as general typos and speech errors. @xcite create a robustness benchmark which considers paraphrasing through word perturbations as well as speech disfluencies. lastly, @xcite considers noise from abbreviations, casing, misspellings, paraphrasing, and synonyms. ## conclusion this paper categorizes the different sources of noise found in dialogue data and studies how models react to them. we find that dialogue noise is divided into issues that occur during training and during inference. we also find that conversations pose unique challenges not found in other nlp corpora, such as discourse naturalness and dialogue breakdowns. our study further reveals that the most common sources of noise are actually based on the ambiguity of individual instances, rather than systematic noise across classes or adversarial annotators actively harming data collection efforts. despite being surprisingly resilient, dialogue models nonetheless experience a notable drop in performance when exposed to high levels of noise. to combat this, we design a proof-of-concept denoising algorithm to serve as a strong foundation for others to compare against. we apply this algorithm successfully to the multiwoz 2.0 dataset, raising the accuracy by 42.5% over the original baseline. we hope our survey informs the collection of cleaner dialogue datasets and the development of advanced denoising algorithms targeting the true sources of dialogue noise.