Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …

The increasing use of artificial intelligence methods in the legal field has sparked interest in
applying Natural Language Processing techniques to handle legal tasks and reduce the …

It is well known that filtering low-quality data before pretraining language models or
selecting suitable data from domains similar to downstream task datasets generally leads to …

This position paper investigates the potential of integrating insights from language
impairment research and its clinical treatment to develop human-inspired learning strategies …

Pre-training serves as a foundation of recent NLP models, where language modeling tasks
are performed over large texts. Typical models like BERT and GPT take the corpus as a …

Pre-training serves as a foundation of recent NLP models, where language modeling task is
performed over large texts. It has been shown that data affects the quality of pre-training, and …

Neural network language model (NNLM) plays an essential role in automatic speech
recognition (ASR) systems, especially in adaptation tasks when text-only data is available. In …

Abstract Knowledge of syntax—the structure of phrases and sentences—is necessary for
robust generalization in natural language processing tasks. One reason for the success of …

In multi-modal task-oriented dialogues, agents need the ability to comprehend the multi-
modal context perceived by the user. For this purpose, Simmc and Simmc2 were introduced …