Training task-oriented dialogue systems typically requires turn-level annotations for interacting with their APIs: e.g. a dialogue state and the system actions taken at each step.These annotations can be costly to produce, error-prone, and require both domain and annotation expertise.With advances in LLMs, we hypothesize that unlabeled data and a schema definition are sufficient for building a working taskoriented dialogue system, completely unsupervised.We consider a novel unsupervised setting of only (1) a well-defined API schema (2) a set of unlabeled dialogues between a user and agent.We propose an innovative approach using expectation-maximization (EM) that infers turn-level annotations as latent variables using a noisy channel model to build an end-to-end dialogue agent.Evaluating our approach on the MultiWOZ benchmark, our method more than doubles the dialogue success rate of a strong GPT-3.5 baseline.1

Summary: Gap-filling is a necessary step to produce quality genome-scale metabolic reconstructions capable of flux-balance simulation. Most available gap-filling tools use an organism-agnostic approach, where reactions are selected from a database to fill gaps without consideration of the target organism. Conversely, our likelihood based gap-filling with probabilistic annotations selects candidate reactions based on a likelihood score derived specifically from the target organism's genome. Here, we present two new implementations of probabilistic annotation and likelihood based gap-filling: a web service called ProbAnnoWeb, and a standalone python package called ProbAnnoPy. Availability and implementation: Our tools are available as a web service with no installation needed (ProbAnnoWeb) at probannoweb.systemsbiology.net, and as a local python package implementation (ProbAnnoPy) at github.com/PriceLab/probannopy. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.