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The Speech Emotion Recognition Adaptation Benchmark (SERAB) is a new framework to evaluate the performance and generalization capacity of different approaches for utterance-level SER. The benchmark is composed of nine datasets for SER in six languages. We used the proposed framework to evaluate a selection of standard hand-crafted feature sets and state-of-the-art DNN representations. The results highlight that using only a subset of the data included in SERAB can result in biased evaluation, while compliance with the proposed protocol can circumvent this issue.

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As an essential task for natural language understanding, slot filling aims to identify the contiguous spans of specific slots in an utterance. In real-world applications, the labeling costs of utterances may be expensive, and transfer learning techniques have been developed to ease this problem. However, cross-domain slot filling could significantly suffer from negative transfer due to non-targeted or zero-shot slots.

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Using the shared-private paradigm and adversarial training
can significantly improve the performance of multi-domain
text classification (MDTC) models. However, there are two
issues for the existing methods: First, instances from the multiple
domains are not sufficient for domain-invariant feature
extraction. Second, aligning on the marginal distributions
may lead to a fatal mismatch. In this paper, we propose mixup
regularized adversarial networks (MRANs) to address these
two issues. More specifically, the domain and category mixup

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