116 citations · 308 across the 15 of their papers we have counts for
15 papers
SpeechDPR: End-to-End Spoken Passage Retrieval for Open-Domain Spoken Question Answering
Chyi-Jiunn Lin, Guan-Ting Lin, Yung-Sung Chuang +5
Spoken Question Answering (SQA) is essential for machines to reply to user's question by finding the answer span within a given spoken passage. SQA has been previously achieved wit…
Multi-task Transformer with Relation-attention and Type-attention for Named Entity Recognition
Ying Mo, Hongyin Tang, Jiahao Liu +5
Named entity recognition (NER) is an important research problem in natural language processing. There are three types of NER tasks, including flat, nested and discontinuous entity…
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…
Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQL
Yuntao Li, Hanchu Zhang, Yutian Li +3
Conversational text-to-SQL aims at converting multi-turn natural language queries into their corresponding SQL (Structured Query Language) representations. One of the most intracta…
Neural Response Generation with Dynamic Vocabularies
Yu Wu, Wei Wu, Dejian Yang +3
We study response generation for open domain conversation in chatbots. Existing methods assume that words in responses are generated from an identical vocabulary regardless of thei…
Hierarchical Recurrent Attention Network for Response Generation
Chen Xing, Wei Wu, Yu Wu +3
We study multi-turn response generation in chatbots where a response is generated according to a conversation context. Existing work has modeled the hierarchy of the context, but d…
Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots
Yu Wu, Wei Wu, Chen Xing +2
We study response selection for multi-turn conversation in retrieval-based chatbots. Existing work either concatenates utterances in context or matches a response with a highly abs…
Knowledge Enhanced Hybrid Neural Network for Text Matching
Yu Wu, Wei Wu, Zhoujun Li +1
Long text brings a big challenge to semantic matching due to their complicated semantic and syntactic structures. To tackle the challenge, we consider using prior knowledge to help…