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Found 11 papers, 4 papers with code
Why Knowledge Distillation Amplifies Gender Bias and How to Mitigate from the Perspective of DistilBERT
Knowledge distillation is widely used to transfer the language understanding of a large model to a smaller model. However, after knowledge distillation, it was found that the smaller model is more biased by gender compared to the source large model. This paper studies what causes gender bias to increase after the knowledge distillation process. Moreover, we suggest applying a variant of the mixup on knowledge distillation, which is used to increase generalizability during the distillation process, not for augmentation. By doing so, we can significantly reduce the gender bias amplification after knowledge distillation. We also conduct an experiment on the GLUE benchmark to demonstrate that even if the mixup is applied, it does not have a significant adverse effect on the model’s performance.
Critic-Guided Decoding for Controlled Text Generation
In this work, we propose a novel critic decoding method for controlled language generation (CriticControl) that combines the strengths of reinforcement learning and weighted decoding.
Beyond Fact Verification: Comparing and Contrasting Claims on Contentious Topics
As the importance of identifying misinformation is increasing, many researchers focus on verifying textual claims on the web.
Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking
To this end, the latest approach is to train a factual consistency classifier on factually consistent and inconsistent summaries.
Plug-and-Play Adaptation for Continuously-updated QA
To this end, we first propose a novel task--Continuously-updated QA (CuQA)--in which multiple large-scale updates are made to LMs, and the performance is measured with respect to the success in adding and updating knowledge while retaining existing knowledge.
SUMBT+LaRL: Effective Multi-domain End-to-end Neural Task-oriented Dialog System
To our best knowledge, our work is the first comprehensive study of a modularized E2E multi-domain dialog system that learning from each component to the entire dialog policy for task success.
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer
Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context.
SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking
In goal-oriented dialog systems, belief trackers estimate the probability distribution of slot-values at every dialog turn.
Ranked #17 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Unpaired Speech Enhancement by Acoustic and Adversarial Supervision for Speech Recognition
Many speech enhancement methods try to learn the relationship between noisy and clean speech, obtained using an acoustic room simulator.
Deep CNNs along the Time Axis with Intermap Pooling for Robustness to Spectral Variations
Convolutional neural networks (CNNs) with convolutional and pooling operations along the frequency axis have been proposed to attain invariance to frequency shifts of features.
Compositional Sentence Representation from Character within Large Context Text
In the HCRN, word representations are built from characters, thus resolving the data-sparsity problem, and inter-sentence dependency is embedded into sentence representation at the level of sentence composition.
