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Domain Robustness in Neural Machine Translation

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

Translating text that diverges from the training domain is a key challenge for machine trans- lation. Domain robustness—the generalization of models to unseen test domains—is low for both statistical (SMT) …...

29144

On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractThe standard training algorithm in neural machine translation (NMT) suffers from exposure bias, and alternative algorithms have been proposed to mitigate this. However, the practical impact of exposure bias is …...

37789

Self-Supervised and Controlled Multi-Document Opinion Summarization

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Publication: TIPSTER (2021)

DOI: nan

AbstractWe address the problem of unsupervised abstractive summarization of collections of user generated reviews through self-supervision and control. We propose a self-supervised setup that considers an individual document as a …...

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Chart-to-Text: A Large-Scale Benchmark for Chart Summarization

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Publication: TIPSTER (2022)

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AbstractCharts are commonly used for exploring data and communicating insights. Generating natural language summaries from charts can be very helpful for people in inferring key insights that would otherwise require …...

39100

Evaluating Open-Domain Question Answering in the Era of Large Language Models

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Publication: TIPSTER (2023)

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AbstractLexical matching remains the de facto evaluation method for open-domain question answering (QA). Unfortunately, lexical matching fails completely when a plausible candidate answer does not appear in the list of …...

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Training Trajectories of Language Models Across Scales

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractScaling up language models has led to unprecedented performance gains, but little is understood about how the training dynamics change as models get larger. How do language models of different …...

37762