33 citations · 83 across the 19 of their papers we have counts for
20 papers
From Token to Line: Enhancing Code Generation with a Long-Term Perspective
Tingwei Lu, Yangning Li, Liyuan Wang +6
The emergence of large language models (LLMs) has significantly promoted the development of code generation task, sparking a surge in pertinent literature. Current research is hind…
PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL
Ruilin Luo, Liyuan Wang, Binghuai Lin +2
Large Language Models (LLMs) have emerged as powerful tools for Text-to-SQL tasks, exhibiting remarkable reasoning capabilities. Different from tasks such as math word problems and…
Making Large Language Models Better Reasoners with Alignment
Peiyi Wang, Lei Li, Liang Chen +5
Reasoning is a cognitive process of using evidence to reach a sound conclusion. The reasoning capability is essential for large language models (LLMs) to serve as the brain of the…
Soft Language Clustering for Multilingual Model Pre-training
Jiali Zeng, Yufan Jiang, Yongjing Yin +5
Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language…
Rhythm-controllable Attention with High Robustness for Long Sentence Speech Synthesis
Dengfeng Ke, Yayue Deng, Yukang Jia +6
Regressive Text-to-Speech (TTS) system utilizes attention mechanism to generate alignment between text and acoustic feature sequence. Alignment determines synthesis robustness (e.g…
Denoising Bottleneck with Mutual Information Maximization for Video Multimodal Fusion
Shaoxiang Wu, Damai Dai, Ziwei Qin +4
Video multimodal fusion aims to integrate multimodal signals in videos, such as visual, audio and text, to make a complementary prediction with multiple modalities contents. Howeve…
DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade
Zefan Cai, Xin Zheng, Tianyu Liu +7
In the constant updates of the product dialogue systems, we need to retrain the natural language understanding (NLU) model as new data from the real users would be merged into the…
Enhancing Continual Relation Extraction via Classifier Decomposition
Heming Xia, Peiyi Wang, Tianyu Liu +3
Continual relation extraction (CRE) models aim at handling emerging new relations while avoiding catastrophically forgetting old ones in the streaming data. Though improvements hav…
Large Language Models are not Fair Evaluators
Peiyi Wang, Lei Li, Liang Chen +7
In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), e.g., GPT-4, as a referee to score and compare the quality of respo…
Bi-Drop: Enhancing Fine-tuning Generalization via Synchronous sub-net Estimation and Optimization
Shoujie Tong, Heming Xia, Damai Dai +5
Pretrained language models have achieved remarkable success in natural language understanding. However, fine-tuning pretrained models on limited training data tends to overfit and…
DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog
Xin Zheng, Tianyu Liu, Haoran Meng +6
Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or…
DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition
Jiali Zeng, Yufan Jiang, Yongjing Yin +3
We present DualNER, a simple and effective framework to make full use of both annotated source language corpus and unlabeled target language text for zero-shot cross-lingual named…
DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection
Haoran Meng, Zheng Xin, Tianyu Liu +6
While interacting with chatbots, users may elicit multiple intents in a single dialogue utterance. Instead of training a dedicated multi-intent detection model, we propose DialogUS…
Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation
Peiyi Wang, Yifan Song, Tianyu Liu +4
Continual relation extraction (CRE) aims to continually learn new relations from a class-incremental data stream. CRE model usually suffers from catastrophic forgetting problem, i.…
Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach
Peiyi Wang, Yifan Song, Tianyu Liu +4
Continual relation extraction (CRE) requires the model to continually learn new relations from class-incremental data streams. In this paper, we propose a Frustratingly easy but Ef…
Text-Aware End-to-end Mispronunciation Detection and Diagnosis
Linkai Peng, Yingming Gao, Binghuai Lin +3
Mispronunciation detection and diagnosis (MDD) technology is a key component of computer-assisted pronunciation training system (CAPT). In the field of assessing the pronunciation…
HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification
Zihan Wang, Peiyi Wang, Tianyu Liu +4
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification due to its complex label hierarchy. Recently, the pretrained language models (PLM)have…
Speech Enhancement using Separable Polling Attention and Global Layer Normalization followed with PReLU
Dengfeng Ke, Jinsong Zhang, Yanlu Xie +2
Single channel speech enhancement is a challenging task in speech community. Recently, various neural networks based methods have been applied to speech enhancement. Among these mo…
A Full Text-Dependent End to End Mispronunciation Detection and Diagnosis with Easy Data Augmentation Techniques
Kaiqi Fu, Jones Lin, Dengfeng Ke +3
Recently, end-to-end mispronunciation detection and diagnosis (MD&D) systems has become a popular alternative to greatly simplify the model-building process of conventional hybrid…
Formant Tracking Using Dilated Convolutional Networks Through Dense Connection with Gating Mechanism
Wang Dai, Jinsong Zhang, Yingming Gao +4
Formant tracking is one of the most fundamental problems in speech processing. Traditionally, formants are estimated using signal processing methods. Recent studies showed that gen…