The ability of zero-shot translation emerges when we train a multilingual model with certain translation directions; the model can then directly translate in unseen directions. Alternatively, zero-shot translation can be accomplished by pivoting through a third language (e.g., English). In our work, we observe that both direct and pivot translations are noisy and achieve less satisfactory performance. We propose EBBS, an ensemble method with a novel bi-level beam search algorithm, where each ensemble component explores its own prediction step by step at the lower level but all components are synchronized by a "soft voting" mechanism at the upper level. Results on two popular multilingual translation datasets show that EBBS consistently outperforms direct and pivot translations, as well as existing ensemble techniques. Further, we can distill the ensemble's knowledge back to the multilingual model to improve inference efficiency; profoundly, our EBBS-distilled model can even outperform EBBS as it learns from the ensemble knowledge.

We address unsupervised discontinuous constituency parsing, where we observe a high variance in the performance of the only previous model in the literature.We propose to build an ensemble of different runs of the existing discontinuous parser by averaging the predicted trees, to stabilize and boost performance.To begin with, we provide comprehensive computational complexity analysis (in terms of P and NP-complete) for tree averaging under different setups of binarity and continuity.We then develop an efficient exact algorithm to tackle the task, which runs in a reasonable time for all samples in our experiments.Results on three datasets show our method outperforms all baselines in all metrics; we also provide in-depth analyses of our approach.1

Knowledge distillation (KD) is the process of transferring knowledge from a large model to a small one.It has gained increasing attention in the natural language processing community, driven by the demands of compressing evergrowing language models.In this work, we propose an f -DISTILL framework, which formulates sequence-level knowledge distillation as minimizing a generalized f -divergence function.We propose four distilling variants under our framework and show that existing SeqKD and ENGINE approaches are approximations of our f -DISTILL methods.We further derive step-wise decomposition for our f -DISTILL, reducing intractable sequence-level divergence to word-level losses that can be computed in a tractable manner.Experiments across four datasets show that our methods outperform existing KD approaches, and that our symmetric distilling losses can better force the student to learn from the teacher distribution.1

Open-domain dialogue systems aim to converse with humans through text, and dialogue research has heavily relied on benchmark datasets. In this work, we observe the overlapping problem in DailyDialog and OpenSubtitles, two popular open-domain dialogue benchmark datasets. Our systematic analysis then shows that such overlapping can be exploited to obtain fake state-of-the-art performance. Finally, we address this issue by cleaning these datasets and setting up a proper data processing procedure for future research.