Hybrid Decoding: Rapid Pass and Selective Detailed Correction for Sequence Models

Recently, Transformer-based encoder-decoder models have demonstrated strong performance in multilingual speech recognition. However, the decoder's autoregressive nature and large size introduce significant bottlenecks during inference. Additionally, although rare, repetition can occur and negatively affect recognition accuracy. To tackle these challenges, we propose a novel Hybrid Decoding approach that both accelerates inference and alleviates the issue of repetition. Our method extends the transformer encoder-decoder architecture by attaching a lightweight, fast decoder to the pretrained encoder. During inference, the fast decoder rapidly generates an output, which is then verified and, if necessary, selectively corrected by the Transformer decoder. This results in faster decoding and improved robustness against repetitive errors. Experiments on the LibriSpeech and GigaSpeech test sets indicate that, with fine-tuning limited to the added decoder, our method achieves word error rates comparable to or better than the baseline, while more than doubling the inference speed.

Suffering from Vaccines or from Government? : Partisan Bias in COVID-19 Vaccine Adverse Events Coverage

Vaccine adverse events have been presumed to be a relatively objective measure that is immune to political polarization. The real-world data, however, shows the correlation between presidential disapproval ratings and the subjective severity of adverse events. This paper investigates the partisan bias in COVID vaccine adverse events coverage with language models that can classify the topic of vaccine-related articles and the political disposition of news comments. Based on 90K news articles from 52 major newspaper companies, we found that conservative media are inclined to report adverse events more frequently than their liberal counterparts, while the coverage itself was statistically uncorrelated with the severity of real-world adverse events. The users who support the conservative opposing party were more likely to write the popular comments from 2.3K random sampled articles on news platforms. This research implies that bipartisanship can still play a significant role in forming public opinion on the COVID vaccine even after the majority of the population's vaccination