Multitask learning (MTL) aims to learn multiple tasks simultaneously while exploiting their
mutual relationships. By using shared resources to simultaneously calculate multiple …

Document retrieval is a key stage of standard Web search engines. Existing dual-encoder
dense retrievers obtain representations for questions and documents independently …

Knowledge in NLP has been a rising trend especially after the advent of large-scale pre-
trained models. Knowledge is critical to equip statistics-based models with common sense …

In the realm of large language models (LLMs), enhancing instruction-following capability
often involves curating expansive training data. This is achieved through two primary …

We present The Vault, a dataset of high-quality code-text pairs in multiple programming
languages for training large language models to understand and generate code. We present …

Leveraging task-aware annotated data as supervised signals to assist with self-supervised
learning on large-scale unlabeled data has become a new trend in pre-training language …

The increasing adoption of natural language processing (NLP) models across industries has
led to practitioners' need for machine learning (ML) systems to handle these models …

The explanation of legal judgments is crucial for the transparency, fairness, and
trustworthiness, aiming to provide rationales for decision-making. While previous works …

Syntactic processing techniques are the foundation of natural language processing (NLP),
supporting many downstream NLP tasks. In this paper, we conduct pair-wise multi-task …

Abstract Natural Language Processing (NLP) is the computational linguistics mode for the
identification and classification of text documents. The NLP process comprises the retrieval …