Large language models (LLMs) have been successfully adapted for interact...
Space-time shift keying-aided orthogonal time frequency space
modulation...
Although large-scale pre-trained language models (PTLMs) are shown to en...
Recently developed large language models have achieved remarkable succes...
In orthogonal time sequency multiplexing (OTSM) modulation, the informat...
We introduce an open-domain topic classification system that accepts
use...
Event temporal reasoning aims at identifying the temporal relations betw...
We consider the problem of Open-world Information Extraction (Open-world...
Traditional sentence embedding models encode sentences into vector
repre...
Existing event-centric NLP models often only apply to the pre-defined
on...
Researchers have proposed various information extraction (IE) techniques...
A spatial modulation-aided orthogonal time frequency space (SM-OTFS) sch...
Although large language models demonstrate remarkable question-answering...
Determining the role of event arguments is a crucial subtask of event
ex...
Despite the success of large language models (LLMs) in various natural
l...
Knowledge base completion (KBC) aims to predict the missing links in
kno...
This paper tackles the problem of how to pre-train a model and make it
g...
Event extraction (EE) is the task of identifying interested event mentio...
Double-blind peer review mechanism has become the skeleton of academic
r...
As a promising architecture, Mobile Data Collector (MDC) enhanced Intern...
Fully-parametric language models generally require a huge number of mode...
In this paper, we propose a comprehensive benchmark to investigate model...
Abstractive summarization models typically learn to capture the salient
...
Large-scale pretrained language models have made significant advances in...
Commonsense Knowledge Base (CSKB) Population aims at reasoning over unse...
In this paper, we propose a new task of sub-event generation for an unse...
Recently, the community has achieved substantial progress on many common...
In this paper, we seek to improve the faithfulness of TempRel extraction...
Natural language often describes events in different granularities, such...
The visual dialog task requires an AI agent to interact with humans in
m...
Knowing the reasoning chains from knowledge to the predicted answers can...
Answering complex logical queries on incomplete knowledge graphs (KGs) w...
Commonsense causality reasoning (CCR) aims at identifying plausible caus...
Online technical forums (e.g., StackOverflow) are popular platforms for
...
Abnormal states in deep reinforcement learning (RL) are states that are
...
Event mentions in text correspond to real-world events of varying degree...
Resolving pronouns to their referents has long been studied as a fundame...
The Winograd Schema (WS) has been proposed as a test for measuring
commo...
Commonsense knowledge acquisition and reasoning have long been a core
ar...
Character linking, the task of linking mentioned people in conversations...
Commonsense knowledge is crucial for artificial intelligence systems to
...
Large-scale pre-trained language models have demonstrated strong knowled...
Identifying events and mapping them to pre-defined event types has long ...
Causality knowledge is crucial for many artificial intelligence systems....
Computational and cognitive studies of event understanding suggest that
...
Understanding natural language involves recognizing how multiple event
m...
This paper studies a new cognitively motivated semantic typing task,
mul...
We address hypernymy detection, i.e., whether an is-a relationship exist...
Pronoun Coreference Resolution (PCR) is the task of resolving pronominal...
Recently, we have seen a rapidly growing adoption of Deep Reinforcement
...