BORT : Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog (2022)

Abstract: A typical end-to-end task-oriented dialog system transfers context into dialog state, and upon which generates a response, which usually faces the problem of error propagation from both previously generated inaccurate dialog states and responses, especially in low-resource scenarios. To alleviate these issues, we propose BORT, a back and denoising reconstruction approach for end-to-end task-oriented dialog system. Squarely, to improve the accuracy of dialog states, back reconstruction is used to reconstruct the original input context from the generated dialog states since inaccurate dialog states cannot recover the corresponding input context. To enhance the denoising capability of the model to reduce the impact of error propagation, denoising reconstruction is used to reconstruct the corrupted dialog state and response. Extensive experiments conducted on MultiWOZ 2.0 and CamRest676 show the effectiveness of BORT. Furthermore, BORT demonstrates its advanced capabilities in the zero-shot domain and low-resource scenarios.

## introduction recently, task-oriented dialog systems, which aim to assist users to complete some booking tasks, have attracted great interest in the research community and the industry @xcite . taskoriented dialog systems have been usually established via a pipeline system, including several modules such as natural language understanding, dialog state tracking, dialog policy, and natural language generation. the natural language understanding module converts user utterance into the structured semantic representation. the dialog state generated by the dialog state tracking module is used to query the database to achieve matched entities. the natural language generation module converts the action state estimated by the dialog policy module to the natural language response. this modular-based architecture is highly interpretable and easy to implement, used in most practical task-oriented dialog systems in the industry. however, every module is optimized individually and doesn't consider the entire dialog history, which affects the performance of the dialog system. therefore, many researchers focus on end-to-end task-oriented dialog systems to train an overall mapping from user natural language input to system natural language output @xcite @xcite @xcite . however, all existing task-oriented dialog systems still suffer from one or more types of error propagation problems from both previously generated inaccurate dialog states and responses, as is illustrated in figure 1 . firstly, the generated dialog state, which is crucial for task completion of task-oriented dialog systems, is usually inaccurate across the end-to-end task-oriented dialog system training. secondly, the previously generated dialog state and response are encoded to create the current dialog state and response during inference, while the oracle previous dialog state and response are encoded during training. there exists a discrepancy between training and inference, affecting the quality of generated system responses. we propose bort, a back and denoising reconstruction approach for end-to-end task-oriented dialog systems to alleviate these issues. to improve dialog state learning ability, back reconstruction is used to reconstruct the generated dialog state back to the original input context to ensure that the information in the input side is completely transformed to the output side. in addition, to enhance the denoising capability of the task-oriented dialog system to reduce the impact of error propagation, denoising reconstruction is used to reconstruct the corrupted dialog state and response. it guarantees that the system learns enough internal information of the dialog context to recover the original version. experimental results on multiwoz 2.0 and camrest676 show that our proposed bort substantially outperforms baseline systems. this paper primarily makes the following contributions: as illustrated in figure 2 (a), we construct an encoder-decoder framework for an end-to-end taskoriented dialog system via dialog state tracking and response generation tasks. one shared encoder encodes dialog context, and two different decoders decode dialog state and system response, respectively. the objective function l all of the entire training process is optimized as: ## methodology in this section, we proposes two reconstruction strategies, i.e., back reconstruction and denoising reconstruction, respectively. generally, during taskoriented dialog training, objective functions l br and l dr are added to enhance model learning ability. the general objective function of a taskoriented dialog system can be reformulated as follows: ## related work end-to-end task-oriented dialog system has attracted much attention in the dialog community. a two-stage copynet framework was proposed to establish an end-to-end task-oriented dialog system based on a single sequence-to-sequence model @xcite . @xcite proposed a multiaction data augmentation framework to improve the diversity of dialog responses. recently, largescale language model pre-training has been effective for enhancing many natural language process-ing tasks @xcite @xcite . decoder-based pre-trained language model such as gpt-2 @xcite was used to improve the performance of endto-end task-oriented dialog system @xcite @xcite . the levenshtein dialog state instead of the dialog state was generated to reduce the inference latency @xcite . in addition, they used encoder-decoderbased pre-trained model such as t5 @xcite and bart @xcite to establish a dialog system. in contrast with previous work, in which system response was generated, @xcite used encoder-based pre-trained model such as bert @xcite for task-oriented dialog system, aiming to retrieve the most relative system response from a candidate pool. reinforcement learning could also be used to enable taskoriented dialog systems to achieve more successful task completion @xcite . @xcite proposed an encoder-decoderreconstructor framework for neural machine translation to alleviate over-translation and undertranslation problems. reconstruction strategy was used to moderate dropped pronoun translation problems @xcite . in contrast, we considered the adequacy of semantic representations rather than natural language sentences to build the reconstruction model. @xcite proposed a denoising autoencoder, in which random noise is added to enhance the robustness of the model, alleviating the overfitting problem of traditional auto-encoder. the denoising auto-encoder strategy was used as the language model to generate more fluent translation candidates for the unsupervised neural machine translation @xcite @xcite . in addition, a denoising auto-encoder was used to pretrain sequence-to-sequence models on the large scale corpus @xcite . in contrast, we proposed a denoising reconstruction mechanism to alleviate the error propagation problem along the multi-turn conversation flow. ## conclusion this paper proposes back and denoising reconstruction strategies for the end-to-end task-oriented dialog system. back reconstruction strategy is proposed to mitigate the generation of inaccurate dialog states, achieving better task completion of the task-oriented dialog system. denoising reconstruction is used to train a robust task-oriented dialog system, further alleviating the error propagation problem. our extensive experiments and analysis on multiwoz 2.0 and camrest676 demonstrate the effectiveness of our proposed reconstruction strategies.