Définition et détection des incohérences du système dans les dialogues orientés tâche. (We present experiments on automatically detecting inconsistent behavior of task-oriented dialogue systems from the context) (2021)

Abstract: Définition et détection des incohérences du système dans les dialogues orientés tâche. Nous présentons des expériences sur la détection automatique des comportements incohérents des systèmes de dialogues orientés tâche à partir du contexte. Nous enrichissons les données bAbI/DSTC2 (Bordes et al., 2017) avec une annotation automatique des incohérences de dialogue, et nous démontrons que les incohérences sont en corrélation avec les dialogues ratés. Nous supposons que l’utilisation d’un historique de dialogue limité et la prédiction du prochain tour de l’utilisateur peuvent améliorer la classification des incohérences. Si les deux hypothèses sont confirmées pour un modèle de dialogue basé sur les réseaux de mémoire, elles ne le sont pas pour un entraînement basé sur le modèle de langage GPT-2, qui bénéficie le plus de l’utilisation de l’historique complet du dialogue et obtient un score de précision de 0,99.

## introduction compared to traditional pipeline architectures, the recent end-to-end neural-network-based dialogue systems have a simpler design and less space for error accumulation, but suffer from less control over the training, reduced explainability and a need for large amounts of training data and computing power, not to mention the difficulty to incorporate external knowledge bases. to address these problems, @xcite developed mem2seq, an end-to-end architecture based on memory networks @xcite for learning from an external database with a relatively small model. @xcite then built wmm2seq, a mem2seq-based dialogue system inspired by cognitive science research, whose architecture is composed of two memory networks, one learning from the dialogue history and the other from a knowledge base. other neural-based works build on two-stage decoding within the same network @xcite . however, neither architecture solves the problem of system inconsistencies inherent in any dialogue generation task. indeed, during a human-machine conversation, it is not uncommon to observe the machine saying something unexpected or inconsistent @xcite . a detection and correction of these inconsistencies is difficult, but would constitute an important improvement since it would allow the system to correct itself @xcite , bringing us one step closer to a lifelong learning architecture @xcite . in this paper, we make the following contributions: (1) we enrich a task-oriented dialogue dataset with inconsistencies annotation, (2) we show that dialogue inconsistencies correlate with failures of the respective dialogues and (3) we perform a series of experiments to train and evaluate inconsistency classification models based on history and user modeling. ## related works in machine translation, @xcite showed that an incremental/simultaneous translation model can get faster by anticipating sequences, with results close to full sentence translation. in dialogue, @xcite reached state-of-the-art results for dialogue act classification by labeling speaker change in dialogue turns during learning, which shows the importance of speaker roles in the conversation. @xcite take this idea one step further by learning to classify the dialogue act of the current and the next dialogue turn, with comparable results. finally, lin et al. (2020) create a system called "imagine then arbitrate" (ita) to learn when to answer and when to listen, by imagining what the user will say to anticipate possible system errors. regarding system error analysis, @xcite model with a pomdp @xcite the uncertainty of a dialogue agent when answering a user question to improve the answer accuracy. @xcite use a natural language inference model to improve a system's consistency in a dialogue, li et al. (2020) then integrate consistency into the system training signal. @xcite apply a similar inference-based approach for dialogue system evaluation. during the dstc6 shared task @xcite , inconsistency detection for non-task-oriented dialogues was one of the problems investigated; however, the inconsistencies found remain quite specific to this type of dialogue. @xcite show that when a conversation exceeds a certain number of dialogue turns, end-to-end systems see their performance decrease, which they attribute to the the conversation history becoming noisy if it is too large. to our knowledge, there has not been a system that predicts the next user's turn and filters dialogue history to anticipate system inconsistencies. ## inconsistency classification we need to distinguish between understanding or decision errors in human-human dialogues, and bot-specific inconsistencies in a human-machine dialogue. indeed, during a task-oriented conversation between two humans, errors or problems lead to almost systematic co-corrections between the two interactors. self-initiated and self-repaired or hetero-initiated co-corrections are applied whenever an error occurs @xcite . however, these errors are corrected thanks to inference, i.e. the explicit errors of a speaker cause the listener to interpret and infer what the first speaker really meant. in the same way, the speaker who made the error infers what the listener could have understood and inferred, in order to self-correct @xcite . in human-machine interaction, this inference is impeded by human knowing that they are talking to a machine with an a priori limited capacity for inference and correction. therefore, when the user or the machine commits human-like errors, this limitation inevitably lowers the confidence and patience on the user part. the main focus in this paper is, however, on inconsistencies -system errors that are not human-like but endemic to the system's learning process. inconsistencies may often cause the user to end the dialogue unsuccessfully (cf. our experiment in section 4.2). for the machine to acquire some co-correction capability, i.e. to be able to detect inconsistencies in its own responses, it must have some representation of the user interpreting them. the dstc2 dialogues are between a real system and users @xcite , and thus include many system inconsistencies, especially based on speech recognition errors. a lot of these inconsistencies are recoverable , so the whole dialogue is not jeopardized. based on an empiric analysis of the corpus, we list the most frequent types of inconsistencies found in human-computer interaction, with an example for each of them (see a lot of the above inconsistencies can be detected using simple rules (e.g., detecting that the system utterance repeats twice in a row). more elaborate rules may concern entity type mismatch between the user and system utterances, etc. the main source of information for all the rules is the dialogue history. however, a conversation history that is too large can become a source of noise for the system @xcite . according to several studies @xcite , the longer a dialogue history is, the more difficult it becomes for the system to retrieve the most relevant information, especially if the slots change during the dialogue. therefore, our goal is to mimic the cognitive forget function @xcite during a dialogue (i.e., reproduce the same information filtering) and to define the optimal dialogue history size to remember. we note that the inconsistency annotation is not turn-independent. for example, in order to detect that the system says the same sentence twice and the user is bothered, we need to know turns t and t + 1 . 1 a legitimate question then is: to what extent can a reduction of the dialogue history size, possibly combined with the knowledge of the user's next turn, allow the system to better detect its own inconsistent behavior? ## conclusion and perspectives this work presents a new dataset based on the dstc2/babi corpus that allows research on the task of detecting dialogue inconsistency, which has not been explored much so far. we conducted experiments that revealed a correlation between system turn inconsistencies and dialogue failures. this fact can be exploited in further research of dialogue modeling to prevent failures. furthermore, we applied four different classifier architectures to automatically detect inconsistencies in the newly formed dataset. among the explored architectures, the best performing were a gpt-2-based classifier and the wmm2seq model. interestingly, while gpt-2 strongly benefits from the provided history context, the wmm2seq performed best when no history was used and next user utterance was available to the model, which makes it more suitable for the real world usecases. access to the next utterance improved results across the board. with this set of experiments, we provide a first proof of the benefit we might gain by having dialogue systems to incorporate an oracle for predicting the next user turn, a step toward a future dialogue architecture with a dual system and user model. in future works, we will evaluate on more complex datasets in order to confirm the usefulness of this new feature when detecting system inconsistencies.