Much recent progress in applications of machine learning models to NLP has been driven
by benchmarks that evaluate models across a wide variety of tasks. However, these broad …

The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …

Highly skewed long-tail item distribution is very common in recommendation systems. It
significantly hurts model performance on tail items. To improve tail-item recommendation …

We review motivations, definition, approaches, and methodology for unsupervised cross-
lingual learning and call for a more rigorous position in each of them. An existing rationale …

Existing algorithms for aligning cross-lingual word vector spaces assume that vector spaces
are approximately isomorphic. As a result, they perform poorly or fail completely on non …

Abstract Machine learning (ML) systems in natural language processing (NLP) face
significant challenges in generalizing to out-of-distribution (OOD) data, where the test …

Fast contextual adaptation has shown to be effective in improving Automatic Speech
Recognition (ASR) of rare words and when combined with an on-device personalized …

Recommendation systems connect users to items and create value chains in the internet
economy. Recommendation systems learn from past user-item interaction histories. As such …

It has been exactly a decade since the first establishment of SPMRL, a research initiative
unifying multiple research efforts to address the peculiar challenges of Statistical Parsing for …

The task of bilingual dictionary induction (BDI) is commonly used for intrinsic evaluation of
cross-lingual word embeddings. The largest dataset for BDI was generated automatically, so …