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Evaluating and Improving Factuality in Multimodal Abstractive Summarization
David Wan, Mohit Bansal
Current metrics for evaluating factuality for abstractive document summarization have achieved high correlations with human judgment, but they do not account for the vision modalit…
FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization
David Wan, Mohit Bansal
We present FactPEGASUS, an abstractive summarization model that addresses the problem of factuality during pre-training and fine-tuning: (1) We augment the sentence selection strat…
Segmenting Subtitles for Correcting ASR Segmentation Errors
David Wan, Chris Kedzie, Faisal Ladhak +6
Typical ASR systems segment the input audio into utterances using purely acoustic information, which may not resemble the sentence-like units that are expected by conventional mach…
Subtitles to Segmentation: Improving Low-Resource Speech-to-Text Translation Pipelines
David Wan, Zhengping Jiang, Chris Kedzie +3
In this work, we focus on improving ASR output segmentation in the context of low-resource language speech-to-text translation. ASR output segmentation is crucial, as ASR systems s…
Incorporating Terminology Constraints in Automatic Post-Editing
David Wan, Chris Kedzie, Faisal Ladhak +2
Users of machine translation (MT) may want to ensure the use of specific lexical terminologies. While there exist techniques for incorporating terminology constraints during infere…