Don’t be Contradicted with Anything! CI - T o D : Towards Benchmarking Consistency for Task-oriented Dialogue System (2021)

Abstract: Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation. However, in contrast to the rapid development in open-domain dialogue, few efforts have been made to the task-oriented dialogue direction. In this paper, we argue that consistency problem is more urgent in task-oriented domain. To facilitate the research, we introduce CI-ToD, a novel dataset for C onsistency I dentification in T ask- o riented D ialog system. In addition, we not only annotate the single label to enable the model to judge whether the system response is contradictory, but also provide more fine-grained labels (i.e., Dialogue History Inconsistency, User Query Inconsistency and Knowledge Base Inconsistency) to encourage model to know what inconsistent sources lead to it. Empirical results show that state-of-the-art methods only achieve 51.3%, which is far behind the human performance of 93.2%, indicating that there is ample room for improving consistency identification ability. Finally, we conduct exhaustive experiments and qualitative analysis to comprehend key challenges and provide guidance for future directions. All datasets and models are publicly available at https://github.com/yizhen20133868/CI-ToD .

## introduction task-oriented dialogue systems (tods) @xcite aim to achieve user goals such as hotel booking and restaurant reservation, has gained more attention recently in both academia and industries. over the last few years, two promising research directions in tods have emerged. the first focuses on a pipeline approach, which consists of modularly connected components @xcite @xcite . the second direction employs an endto-end model, which directly takes the sequence-tosequence (seq2seq) model to generate a response from a dialogue history and a corresponding knowledge base (kb) @xcite @xcite @xcite in recent years, with the burst of deep neural networks and the evolution of pre-trained language models, the research of tods has obtained great success. while the success is indisputable, previous work have shown that it's inevitable to generate inconsistent response with the neural-based model, resulting in a contradiction @xcite @xcite . such contradictions caused by these bots are often jarring, immediately disrupt the conversational flow. to address the above issue, some work try to improve consistency in dialogue by posing a consistency identification into dialogue. @xcite made an early step towards performing consistency identification in dialogue agent. @xcite proposed dialogue contradiction detection task to prevent the system response from being inconsistent with dialogue history. @xcite further proposed a profile consistency identification to consider whether response is consistent with the corresponding profile. though achieving the promising performance, the above work were limited to open-domain dialogue. in this paper, we highlight that inconsistent generation problems should also be considered in task-oriented dialogue. for example, as shown in figure 1 , the system expresses about the poi whole foods in dialogue history. however, when we run the state-of-the-art model (df-net) @xcite , the system generate response "mandarin roots is located at 271 springer street.", which incorrectly generates irrelevant poi mandarin roots, resulting in contradiction. this is because neural-based models are a black-box and thus make us hard to explicitly control the neural-based dialogue systems to maintain a consistent response generation. from the user's perspective, such inconsistent bots not only fail to meet the requirements of the user but also mislead users to get wrong feedback in the task-oriented domain. therefore, it's promising to consider consistency problem and detect in advance whether the generated response is consistent in task-oriented dialogue direction. unfortunately, there still has been relatively little research on considering consistency identification in task-oriented dialogue due to the the lacking of public benchmarks. to fill this research gap, we introduce a novel human-annotated dataset ci-tod: consistency identification in task-oriented dialog system. dialogue data for ci-tod is collected from the public dialogue corpora kvret @xcite . for each final system response in kvret, we re-write the utterance by crowdsourcing where we deliberately contradict the dialogue history, user query or the corresponding knowledge base (kb). as shown in to establish baseline performances on ci-tod, we evaluate the state-of-the-art pre-trained and non pre-trained models for consistency identification. experimental results demonstrate a significant gap between machine and human performance, indicating there is ample room for improving consistency identification ability. in addition, we show that our best consistency identification detector correlates well with human judgements, demonstrating that it can be suitable for use as an automatic metric for checking task-oriented dialogue consistency. finally, we perform exhaustive experiments and qualitative analysis to shed light on the challenges that current approaches faced with ci-tod. in summary, our contributions are three-fold: ## problem formulation in our paper, the consistency identification in taskoriented dialogue is formulated as a supervised multi-label classification task, which aims to judge whether the generated system response is inconsistent. to equip the model with the ability to analyze what the inconsistent sources lead to it, we require the model not only provide the final prediction but also the fine-grained sources including dialogue history, knowledge base (kb) and user's where is the nearest gas station? what is the address? the 76 gas station is located at 611 ames ave. where is the nearest gas station? what is the address? the 76 grocery store is located at 611 ames ave. where is the nearest gas station? what is the address? the 76 grocery store is located at 611 ames ave. it is 3 miles away where is the nearest gas station? what is the address? the 76 grocery store is located at 611 ames ave. it is 3 miles away ✘hi ✘kbi (a) original dialog (b) kbi annotation (c) hi and qi annotation (d) human re-check recheck ✘kbi ✘hi ✘kbi poi poi_type address distance 76 gas station 611 ames ave 4 miles poi poi_type address distance 76 gas station 611 ames ave 4 miles poi poi_type address distance 76 gas station 611 ames ave 4 miles poi poi_type address distance 76 gas station 611 ames ave 4 miles *re-written utterance poi … traffic_info valero … road_block_nearby willows_market … no_traffic [usr] address to the gas_station [sys] valero is located at 200_alester_ave [usr] ok please give me directions to valero via a route that avoids all heavy_traffic [sys] i found a route for you to willows_market even though with heavy_traffic ✘(kbi) ✘(qi) ✘(hi) query. more specifically, given a task-oriented dialogue between a user (u) and a system (s), the n-turned dialogue snippet consists of dialogue history @xmath0, s 1 ), (u 2 , s 2 ), ..., (u n-1 , s n-1 )}, the corresponding knowledge base kb, the user query u n and system response s n . more specifically, the task can be defined as: ## dataset we construct the ci-tod dataset based on the kvret dataset and follow four steps: (a) data pre-processing, (b) kbi construction, (c) qi and hi construction and (d) human annotation, which is illustrated in figure 2 . in the following, we first describe the definition of qi, hi and kbi, then illustrate the four construction steps in detail. ## models in this section, we establish several strong baseline methods using the state-of-the-art non pre-trained models ( §4.1) and pre-trained models ( §4.2). since multi-task framework has obtained remarkable success on various nlp tasks @xcite @xcite @xcite , we adopt a vanilla multitask framework to simultaneously perform qi, hi, and kbi, which has the advantage of extracting the shared knowledge across three tasks. for both pre-trained models and non-pre-trained models, we introduce delimiter tokens [sok], [usr] and [sys] to signal the beginning of kb, user utterance and system response, respectively, aiming to learn to distinguish the role of kb, user and system behavior in multi-turn dialogues. specifically, the input of kb is denoted as @xmath1 ## related work this work is related to the considering consistency in open-domain dialogue. in recent years, several personalized dialogue datasets have been introduced, such as personachat @xcite and personaldialog @xcite . these datasets are able to implicitly consider the consistency in dialogue generation, but fail to explicitly teach the model to judge whether the generated system response is consistent. another series of related work explicity improve consistency in dialogue. to this end, some benchmarks have been proposed to promote the research. @xcite made an early step towards reducing the dialogue consistency identification to natural language inference (nli). @xcite presented a novel paradigm for evaluating the coherence of dialogue systems by using stateof-the-art entailment techniques and build a synthesized dataset inferconvai geared toward evaluating consistency in dialogue systems. @xcite introduced the dialogue contradiction de-tection task (decode) and a new conversational dataset containing contradictory dialogues, aiming to evaluate the ability to detect contradictory. @xcite proposed a kvpi dataset and profile consistency identification task for open-domain dialogue agents to further evaluate whether the system response is inconsistent with the corresponding profile information. compared with their work that mainly focus on the open-domain dialogue direction, we aim to fill the gap of consistency identification in task-oriented dialogue systems. furthermore, we introduce a human-annotated dataset to this end. besides, we provide some key challenges and future directions to facilitate further research. ## conclusion we studied consistency identification in taskoriented dialogue and introduced a new humanannotated dataset ci-tod. further, we analyzed the problems of ci-tod through extensive experiments and highlight the key challenges of the task. we hope ci-tod can facilitate future research on consistency identification in task-oriented dialogue.