2 research outputs found
Know Thy Strengths: Comprehensive Dialogue State Tracking Diagnostics
Recent works that revealed the vulnerability of dialogue state tracking (DST)
models to distributional shifts have made holistic comparisons on robustness
and qualitative analyses increasingly important for understanding their
relative performance. We present our findings from standardized and
comprehensive DST diagnoses, which have previously been sparse and
uncoordinated, using our toolkit, CheckDST, a collection of robustness tests
and failure mode analytics. We discover that different classes of DST models
have clear strengths and weaknesses, where generation models are more promising
for handling language variety while span-based classification models are more
robust to unseen entities. Prompted by this discovery, we also compare
checkpoints from the same model and find that the standard practice of
selecting checkpoints using validation loss/accuracy is prone to overfitting
and each model class has distinct patterns of failure. Lastly, we demonstrate
how our diagnoses motivate a pre-finetuning procedure with non-dialogue data
that offers comprehensive improvements to generation models by alleviating the
impact of distributional shifts through transfer learning.Comment: EMNLP202
SeamlessM4T-Massively Multilingual & Multimodal Machine Translation
What does it take to create the Babel Fish, a tool that can help individuals
translate speech between any two languages? While recent breakthroughs in
text-based models have pushed machine translation coverage beyond 200
languages, unified speech-to-speech translation models have yet to achieve
similar strides. More specifically, conventional speech-to-speech translation
systems rely on cascaded systems that perform translation progressively,
putting high-performing unified systems out of reach. To address these gaps, we
introduce SeamlessM4T, a single model that supports speech-to-speech
translation, speech-to-text translation, text-to-speech translation,
text-to-text translation, and automatic speech recognition for up to 100
languages. To build this, we used 1 million hours of open speech audio data to
learn self-supervised speech representations with w2v-BERT 2.0. Subsequently,
we created a multimodal corpus of automatically aligned speech translations.
Filtered and combined with human-labeled and pseudo-labeled data, we developed
the first multilingual system capable of translating from and into English for
both speech and text. On FLEURS, SeamlessM4T sets a new standard for
translations into multiple target languages, achieving an improvement of 20%
BLEU over the previous SOTA in direct speech-to-text translation. Compared to
strong cascaded models, SeamlessM4T improves the quality of into-English
translation by 1.3 BLEU points in speech-to-text and by 2.6 ASR-BLEU points in
speech-to-speech. Tested for robustness, our system performs better against
background noises and speaker variations in speech-to-text tasks compared to
the current SOTA model. Critically, we evaluated SeamlessM4T on gender bias and
added toxicity to assess translation safety. Finally, all contributions in this
work are open-sourced and accessible at
https://github.com/facebookresearch/seamless_communicatio