![]() ![]() Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. However, these networks are heavily reliant on big data to avoid overfitting. Our approach also consistently and substantially obtained 1.0 to 2.0 BLEU improvement in three other low-resource translation tasks, including English–Turkish, Nepali–English, and Sinhala–English translation tasks.Ībstract Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. In the experiment, the proposed approach achieved 1.96 BLEU points in the IWSLT2014 German–English translation tasks, which was used to simulate a low-resource language. Finally, we filter and merge origin data and synthetic parallel corpus to train the final model. To generate diversity data, the restricted sampling strategy is employed at the decoding steps. We expand the training data by generating diversity pseudo parallel data on the source and target sides. In this paper, we propose a diversity data augmentation method that does not use extra monolingual data. For low-resource languages, the amount of parallel data is not sufficient, which results in poor translation quality. One important issue that affects the performance of neural machine translation is the scale of available parallel data. We release our code to support future work in this direction. Moreover, they allow the model to perform better than baselines while accessing fewer utterances from the opponent. We find that the proposed data adaptations lead to strong performance in zero-shot and few-shot scenarios. We show the utility of our proposed approach through extensive experiments based on two dialogue datasets. We further devise ways to adapt related data sources for this task to provide more explicit supervision for incorporating the opponent's preferences and offers, as a proxy to relying on granular utterance-level annotations. The model takes in a partial dialogue as input and predicts the priority order of the opponent. In this work, we propose a ranker for identifying these priorities from negotiation dialogues. ![]() A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training. In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals. Opponent modeling is the task of inferring another party's mental state within the context of social interactions.
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