3 accepted papers
Neural machine translation (MT) models achieve strong results across a variety of settings, but it is widely believed that they are highly sensitive to “noisy” inputs, such as spelling errors, abbreviations, and other formatting issues. In this paper, we revisit this insight in light of recent multi…
We introduce Spire, a speech-augmented language model (LM) capable of both translating and transcribing speech input from English into 10 other languages as well as translating text input in both language directions. Spire integrates the speech modality into an existing multilingual LM via speech di
Current sequence-to-sequence models are trained to minimize cross-entropy and use softmax to compute the locally normalized probabilities over target sequences. While this setup has led to strong results in a variety of tasks, one unsatisfying aspect is its length bias: models give high scores to sh…