In this work, we use large language models (LLMs) to augment and acceler...
Reasoning is a cognitive process of using evidence to reach a sound
conc...
We uncover a systematic bias in the evaluation paradigm of adopting larg...
Entity linking models have achieved significant success via utilizing
pr...
Recently, Pretrained Language Models (PLMs) have been serving as
general...
Pretrained language models have achieved remarkable success in a variety...
Video multimodal fusion aims to integrate multimodal signals in videos, ...
Generative Language Models (GLMs) have demonstrated capabilities to stor...
Continual learning (CL) aims to constantly learn new knowledge over time...
Continual relation extraction (CRE) models aim at handling emerging new
...
With the increasing ability of large language models (LLMs), in-context
...
Large pretrained language models have shown surprising In-Context Learni...
Datasets serve as crucial training resources and model performance track...
Harvesting question-answer (QA) pairs from customer service chatlog in t...
While interacting with chatbots, users may elicit multiple intents in a
...
Continual relation extraction (CRE) aims to continually learn new relati...
Previous literature has proved that Pretrained Language Models (PLMs) ca...
Continual relation extraction (CRE) requires the model to continually le...
The ability of pretrained Transformers to remember factual knowledge is
...
Fine-tuning pretrained language models (PLMs) on downstream tasks has be...
Most previous studies aim at extracting events from a single sentence, w...
Hierarchical text classification (HTC) is a challenging subtask of
multi...
As Abstract Meaning Representation (AMR) implicitly involves compound
se...
The Mixture-of-Experts (MoE) technique can scale up the model size of
Tr...
Biomedical Question Answering (BQA) has attracted increasing attention i...
In this paper, we propose Generalized Aggressive Decoding (GAD) – a nove...
Abstract Meaning Representation (AMR) parsing translates sentences to th...
Few-Shot Sequence Labeling (FSSL) is a canonical solution for the taggin...
Few-Shot Event Classification (FSEC) aims at developing a model for even...
Aspect Sentiment Triplet Extraction (ASTE) aims to recognize targets, th...
Artificial Intelligence (AI), along with the recent progress in biomedic...
Evaluation in natural language processing guides and promotes research o...
Large pretrained generative models like GPT-3 often suffer from hallucin...
Large-scale pretrained language models are surprisingly good at recallin...
Conventional Machine Reading Comprehension (MRC) has been well-addressed...
In open domain table-to-text generation, we notice that the unfaithful
g...
Document-level Relation Extraction (RE) requires extracting relations
ex...
The prior work on natural language inference (NLI) debiasing mainly targ...
While discriminative neural network classifiers are generally preferred,...
Conventional Knowledge Graph Completion (KGC) assumes that all test enti...
Many recent studies have shown that for models trained on datasets for
n...
While many BERT-based cross-modal pre-trained models produce excellent
r...
Learning to navigate in a visual environment following natural language
...
In this paper, we focus on the task of generating a pun sentence given a...
Unsupervised text style transfer aims to transfer the underlying style o...
Word Sense Disambiguation (WSD) aims to identify the correct meaning of
...
Table-to-text generation aims to generate a description for a factual ta...
Generating texts from structured data (e.g., a table) is important for
v...
Previous studies on Chinese semantic role labeling (SRL) have concentrat...
Without discourse connectives, classifying implicit discourse relations ...