The Importance of Language Models in Automatic Speech Recognition

Rachael Tatman is a senior developer advocate for Rasa, where she’s helping developers build and deploy ML chatbots using their open source framework.

Rachael has a PhD in Linguistics from the University of Washington where her research was on computational sociolinguistics, or how our social identity affects the way we use language in computational contexts. Previously she was a data scientist at Kaggle and she’s still a Kaggle Grandmaster.

In this conversation, Rachael and I talk about the history of NLP and conversational AI//chatbots and we dive into the fascinating tension between rule-based techniques and ML and deep learning – we also talk about how to incorporate machine and human intelligence together by thinking through questions such as “should a response to a human ever be automated?” Spoiler alert: the answer is a resounding NO WAY!

In this journey, something that becomes apparent is that many of the trends, concepts, questions, and answers, although framed for NLP and chatbots, are applicable to much of data science, more generally.

We also discuss the data scientist’s responsibility to end-users and stakeholders using, among other things, the lens of considering those whose data you’re working with to be data donors.

We then consider what globalized language technology looks like and can look like, what we can learn from the history of science here, particularly given that so much training data and models are in English when it accounts for so little of language spoken globally.

Links

• Rachael's website
• Rasa
• Speech and Language Processing by Dan Jurafsky and James H. Martin
  • Masakhane, putting African languages on the #NLP map since 2019
  • The Distributed AI Research Institute, a space for independent, community-rooted AI research, free from Big Tech’s pervasive influence
  • The Algorithmic Justice League, unmasking AI harms and biases
  • Black in AI, increasing the presence and inclusion of Black people in the field of AI by creating space for sharing ideas, fostering collaborations, mentorship and advocacy
  • Hugo's blog post on his new job and why it's exciting for him to double down on helping scientists do better science