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You possibly can nonetheless set objectives with Nike Fuel and see charts and graphs depicting your workouts, however the focus of the FuelBand experience is on that custom number. Using an interpretation-to-textual content mannequin for paraphrase generation, we're able to depend on existing dialog system coaching knowledge, and, in combination with shuffling-based mostly sampling techniques, we will obtain various and novel paraphrases from small quantities of seed information. However, in evolving real-world dialog techniques, where new functionality is frequently added, a serious further challenge is the lack of annotated coaching data for such new functionality, as the required knowledge assortment efforts are laborious and time-consuming. Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for brand new Features in Task-Oriented Dialog Systems Shailza Jolly author Tobias Falke creator Caglar Tirkaz creator Daniil Sorokin creator 2020-dec text Proceedings of the 28th International Conference on Computational Linguistics: Industry Track International Committee on Computational Linguistics Online conference publication Recent progress by advanced neural fashions pushed the efficiency of process-oriented dialog techniques to virtually excellent accuracy on present benchmark datasets for intent classification and slot labeling.<br><br><br><br> We conduct experiments on multiple conversational datasets and show important improvements over present methods together with recent on-system fashions. In addition, the combination of our BJAT with BERT-giant achieves state-of-the-art outcomes on two datasets. Our results on reasonable situations using a commercial route solver recommend that machine studying could be a promising way to assess the feasibility of customer insertions. Experimental results and ablation studies also present that our neural fashions preserve tiny reminiscence footprint necessary to function on sensible gadgets, whereas still sustaining excessive performance. However, many joint fashions nonetheless suffer from the robustness problem, especially on noisy inputs or uncommon/unseen events. To handle this challenge, we suggest a Joint Adversarial Training (JAT) mannequin to enhance the robustness of joint intent detection and slot filling, which consists of two elements: (1) mechanically producing joint adversarial examples to assault the joint model, and (2) coaching the mannequin to defend towards the joint adversarial examples so as to robustify the mannequin on small perturbations. Extensive experiments and analyses on the lightweight fashions show that our proposed strategies achieve significantly larger scores and substantially enhance the robustness of each intent detection and slot filling.<br>