A ny TOD : A Programmable Task-Oriented Dialog System (2023)

Abstract: We propose AnyTOD, an end-to-end, zero-shot task-oriented dialog (TOD) system capable of zero-shot adaptation onto unseen tasks or domains. We view TOD as a program executed by a language model (LM), where program logic and ontology is provided by a designer as a schema. To enable generalization to unseen schemas and programs without prior training, AnyTOD adopts a neuro-symbolic approach. A neural LM keeps track of events that occur during a conversation, and a symbolic program implementing dialog policy is executed to recommend actions AnyTOD should take. This approach drastically reduces data annotation and model training requirements, addressing the enduring challenge of rapidly adapting a TOD system to unseen tasks and domains. We demonstrate state-of-the-art results on STAR, ABCD and SGD benchmarks. We also demonstrate strong zero-shot transfer ability in low-resource settings, such as zero-shot transfer onto MultiWOZ. In addition, we release STARv2, an updated version of the STAR dataset with richer annotations, for benchmarking zero-shot task transfer for end-to-end TOD models.

## introduction an enduring challenge in building and maintaining task-oriented dialog (tod) systems is efficiently adapting to a new task or domain. for instance, if we were to add the ability to book flight tickets to an existing system that can only handle booking train tickets, this requires manual data collection and labeling for new conversations about flight booking, as well as retraining of natural language understanding (nlu) and policy models. these data efficiency and scaling problems compound for figure 1 : an overview of the anytod system. a lm conducts zero-shot state and action tracking with respect to a provided schema, abstracting it into a sequence of symbols. a program that executes the dialog policy then recommends which actions to take based on the states sequence, the lm then chooses a single final action and generating a response. to tackle this problem, we propose anytod, an end-to-end tod system that can be programmed to adapt to unseen tasks or domains without prior training, significantly reducing the data collection and training requirements for enabling new tod 1 16189 tasks. to the best of our knowledge, anytod is the first end-to-end tod system capable of zeroshot transfer. to this end, we view tod as a program that a language model (lm) must execute throughout a conversation, and can rely on to provide guidance. anytod can be controlled by any predefined task policy if implemented as a program, allowing arbitrary business logic to be executed for a specific task. to demonstrate the efficacy of this paradigm, we experiment with the star @xcite , abcd @xcite , sgd @xcite and multiwoz @xcite benchmarks. we show that anytod achieves state-of-the-art results in both full-shot and zero-shot transfer settings. overview of anytod to adhere to a given program, anytod adopts a neuro-symbolic approach (figure 1 ). a neural lm is trained for dialog state tracking (dst) and action state tracking (ast), abstracting both states and actions into a sequence of symbols. to support zero-shot task adaptation, we follow the schema-guided paradigm advocated by @xcite , which provides a schema to the lm as contextual information, describing all parameters and actions that should be tracked in natural language. by training on a large corpus of diverse schemas, the lm generalizes to arbitrary and unseen schemas @xcite . a schema should also provide a symbolic program that declares the task logic, which is executed to recommend possible next actions the agent can take, conditioned on the current dialog states. these recommendations are then reincorporated into the lm, which selects a single next action prediction (nap), and generates a response. note that the symbolic program forces anytod to consider a dialog policy explicitly, driving zeroshot transfer onto unseen policies and allowing arbitrarily complex business logic to be employed. however, the program's recommendations are only considered as guidelines, and it is up to the lm to make a final decision on the nap. ## conclusion we proposed anytod, an end-to-end tod system that can be programmed to adapt to unseen tasks without domain-specific training. anytod adopts a neuro-symbolic approach, in which a lm performs robust dst and ast with respect to a provided schema, and abstracts both into a sequence of symbols. these symbol sequences are then parsed and passed to an explicity symbolic program implementing a task's dialog policy, which is executed to make recommendations for the next agent action(s). agent designers are free to implement arbitrarily complex business logic this program any-tod to determine its policy on unseen tasks or domains. to demonstrate the value of this approach, we show state-of-the-art results on zero-shot transfer tod benchmarks, such as star, abcd, sgd and multiwoz. for further training and benchmarking zero-shot end-to-end tod systems, we also release the starv2 dataset, an improved version of star.