Shyam Upadhyay

TL;DR: The dataset is the first to study multi-sentence inference at scale, with an open-ended set of question types that requires reasoning skills, and finds human solvers to achieve an F1-score of 88.1%.

TL;DR: Evaluation of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters finds that model performance and calibration both improve with scale, but are poor in absolute terms.

TL;DR: This article performed an extensive evaluation of four popular approaches of inducing cross-lingual embeddings, each requiring a different form of supervision, on four typographically different language pairs, and showed that models which require expensive crosslingual knowledge almost always perform better, but cheaply supervised models often prove competitive on certain tasks.

TL;DR: This work attempts to fill the gap by giving a comprehensive explanation which justifies both kinds of observations (i.e., when is attention interpretable and when it is not) and reinforces the claim of interpretability of attention through manual evaluation.

TL;DR: Different approaches to train a SLU component with little supervision for two new languages - Hindi and Turkish are examined, and it is shown that with only a few hundred labeled examples the authors can surpass the approaches proposed in the literature.