An Imitation Game for Learning Semantic Parsers from User Interaction

Despite the widely successful applications, bootstrapping and fine-tuning semantic parsers are still a tedious process with challenges such as costly data annotation and privacy risks. In this paper, we suggest an alternative, human-in-the-loop methodology for learning semantic parsers directly from users. A semantic parser should be introspective of its uncertainties and prompt for user demonstration when uncertain. In doing so it also gets to imitate the user behavior and continue improving itself autonomously with the hope that eventually it may become as good as the user in interpreting their questions. To combat the sparsity of demonstration, we propose a novel annotation-efficient imitation learning algorithm, which iteratively collects new datasets by mixing demonstrated states and confident predictions and re-trains the semantic parser in a Dataset Aggregation fashion (Ross et al., 2011). We provide a theoretical analysis of its cost bound and also empirically demonstrate its promising performance on the text-to-SQL problem.

On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

Adaptive gradient methods are workhorses in deep learning. However, the convergence guarantees of adaptive gradient methods for nonconvex optimization have not been sufficiently studied. In this paper, we provide a sharp analysis of a recently proposed adaptive gradient method namely partially adaptive momentum estimation method (Padam) (Chen and Gu, 2018), which admits many existing adaptive gradient methods such as AdaGrad, RMSProp and AMSGrad as special cases. Our analysis shows that, for smooth nonconvex functions, Padam converges to a first-order stationary point at the rate of $O\big((\sum_{i=1}^d\|\mathbf{g}_{1:T,i}\|_2)^{1/2}/T^{3/4} + d/T\big)$, where $T$ is the number of iterations, $d$ is the dimension, $\mathbf{g}_1,\ldots,\mathbf{g}_T$ are the stochastic gradients, and $\mathbf{g}_{1:T,i} = [g_{1,i},g_{2,i},\ldots,g_{T,i}]^\top$. Our theoretical result also suggests that in order to achieve faster convergence rate, it is necessary to use Padam instead of AMSGrad. This is well-aligned with the empirical results of deep learning reported in Chen and Gu (2018).

Label Propagation on K-partite Graphs with Heterophily

In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption. Homophily label propagation (i.e., two connected nodes share similar labels) in homogeneous graph (with same types of vertices and relations) has been extensively studied before. Unfortunately, real-life networks are heterogeneous, they contain different types of vertices (e.g., users, images, texts) and relations (e.g., friendships, co-tagging) and allow for each node to propagate both the same and opposite copy of labels to its neighbors. We propose a $\mathcal{K}$-partite label propagation model to handle the mystifying combination of heterogeneous nodes/relations and heterophily propagation. With this model, we develop a novel label inference algorithm framework with update rules in near-linear time complexity. Since real networks change over time, we devise an incremental approach, which supports fast updates for both new data and evidence (e.g., ground truth labels) with guaranteed efficiency. We further provide a utility function to automatically determine whether an incremental or a re-modeling approach is favored. Extensive experiments on real datasets have verified the effectiveness and efficiency of our approach, and its superiority over the state-of-the-art label propagation methods.