Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …

While recent language models have the ability to take long contexts as input, relatively little
is known about how well they use longer context. We analyze the performance of language …

We introduce a new large-scale NLI benchmark dataset, collected via an iterative,
adversarial human-and-model-in-the-loop procedure. We show that training models on this …

Recent years have seen impressive performance of transformer-based models on different
natural language processing tasks. However, it is not clear to what degree the transformers …

We introduce Dynabench, an open-source platform for dynamic dataset creation and model
benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the …

Do state-of-the-art natural language understanding models care about word order-one of the
most important characteristics of a sequence? Not always! We found 75% to 90% of the …

Towards building intelligent dialogue agents, there has been a growing interest in
introducing explicit personas in generation models. However, with limited persona-based …

Transformer-based language models benefit from conditioning on contexts of hundreds to
thousands of previous tokens. What aspects of these contexts contribute to accurate model …

Nowadays, open-domain dialogue models can generate acceptable responses according to
the historical context based on the large-scale pre-trained language models. However, they …

In the machining industry, tool wear has a great influence on machining efficiency, product
quality, and production costs. To achieve accurate tool wear estimation, a novel CNN …