Semantic representation is receiving growing attention in NLP in the past few years, and
many proposals for semantic schemes (eg, AMR, UCCA, GMB, UDS) have been put forth …

Large language models (LLMs) exhibit impressive capabilities across diverse tasks in
natural language processing. Nevertheless, challenges arise such as large model …

Place names embedded in online natural language text present a useful source of
geographic information. Despite this, many methods for the extraction of place names from …

Inferring spatial relations in natural language is a crucial ability an intelligent system should
possess. The bAbI dataset tries to capture tasks relevant to this domain (task 17 and 19) …

Recent research shows synthetic data as a source of supervision helps pretrained language
models (PLM) transfer learning to new target tasks/domains. However, this idea is less …

The use of knowledge graphs as a data source for machine learning methods to solve
complex problems in life sciences has rapidly become popular in recent years. Our …

Multi-task learning with Convolutional Neural Network (CNN) has shown great success in
many Natural Language Processing (NLP) tasks. This success can be largely attributed to …

Visual Spatial Description (VSD) is an emerging image-to-text task which aims at generating
descriptions of the spatial relationships between given objects in an image. In this paper, we …

Spatial information extraction is essential to understand geographical information in text.
This task is largely divided to two subtasks: spatial element extraction and spatial relation …

Image-to-text tasks, such as open-ended image captioning and controllable image
description, have received extensive attention for decades. Here, we further advance this …