116 citations · 308 across the 15 of their papers we have counts for
8 papers · 1 filter
SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks
Suwon Shon, Siddhant Arora, Chyi-Jiunn Lin +7
Spoken language understanding (SLU) tasks have been studied for many decades in the speech research community, but have not received as much attention as lower-level tasks like spe…
XPrompt: Exploring the Extreme of Prompt Tuning
Fang Ma, Chen Zhang, Lei Ren +5
Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for performing downstream tasks in a parameter-efficient manner. While prompt tuning has gr…
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations
Borun Chen, Hongyin Tang, Jiahao Bu +6
Pre-trained Language Models (PLMs) have achieved remarkable performance gains across numerous downstream tasks in natural language understanding. Various Chinese PLMs have been suc…
Structural Bias for Aspect Sentiment Triplet Extraction
Chen Zhang, Lei Ren, Fang Ma +3
Structural bias has recently been exploited for aspect sentiment triplet extraction (ASTE) and led to improved performance. On the other hand, it is recognized that explicitly inco…
Making Pretrained Language Models Good Long-tailed Learners
Chen Zhang, Lei Ren, Jingang Wang +2
Prompt-tuning has shown appealing performance in few-shot classification by virtue of its capability in effectively exploiting pre-trained knowledge. This motivates us to check the…
TANet: Thread-Aware Pretraining for Abstractive Conversational Summarization
Ze Yang, Liran Wang, Zhoujin Tian +2
Although pre-trained language models (PLMs) have achieved great success and become a milestone in NLP, abstractive conversational summarization remains a challenging but less studi…
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER
Liwen Wang, Rumei Li, Yang Yan +4
Recently, prompt-based methods have achieved significant performance in few-shot learning scenarios by bridging the gap between language model pre-training and fine-tuning for down…
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis
Kai Zhang, Kun Zhang, Mengdi Zhang +4
Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in the given sentence. While pre-trained language models such as BERT have achieved gre…