1.2k citations · 2.9k across the 87 of their papers we have counts for
14 papers · 2 filters
MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing
Longxu Dou, Yan Gao, Mingyang Pan +4
Text-to-SQL semantic parsing is an important NLP task, which greatly facilitates the interaction between users and the database and becomes the key component in many human-computer…
A Survey on Table-and-Text HybridQA: Concepts, Methods, Challenges and Future Directions
Dingzirui Wang, Longxu Dou, Wanxiang Che
Table-and-text hybrid question answering (HybridQA) is a widely used and challenging NLP task commonly applied in the financial and scientific domain. The early research focuses on…
A Survey on Natural Language Processing for Programming
Qingfu Zhu, Xianzhen Luo, Fang Liu +2
Natural language processing for programming aims to use NLP techniques to assist programming. It is increasingly prevalent for its effectiveness in improving productivity. Distinct…
LERT: A Linguistically-motivated Pre-trained Language Model
Yiming Cui, Wanxiang Che, Shijin Wang +1
Pre-trained Language Model (PLM) has become a representative foundation model in the natural language processing field. Most PLMs are trained with linguistic-agnostic pre-training…
MetaPrompting: Learning to Learn Better Prompts
Yutai Hou, Hongyuan Dong, Xinghao Wang +2
Prompting method is regarded as one of the crucial progress for few-shot nature language processing. Recent research on prompting moves from discrete tokens based ``hard prompts''…
Overview of CTC 2021: Chinese Text Correction for Native Speakers
Honghong Zhao, Baoxin Wang, Dayong Wu +3
In this paper, we present an overview of the CTC 2021, a Chinese text correction task for native speakers. We give detailed descriptions of the task definition and the data for tra…
Language Anisotropic Cross-Lingual Model Editing
Yang Xu, Yutai Hou, Wanxiang Che +1
Multilingual pre-trained language models can learn task-specific abilities or memorize facts across multiple languages but inevitably make undesired predictions with specific input…
GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding
Libo Qin, Qiguang Chen, Tianbao Xie +4
Due to high data demands of current methods, attention to zero-shot cross-lingual spoken language understanding (SLU) has grown, as such approaches greatly reduce human annotation…
Improving Pre-trained Language Models with Syntactic Dependency Prediction Task for Chinese Semantic Error Recognition
Bo Sun, Baoxin Wang, Wanxiang Che +3
Existing Chinese text error detection mainly focuses on spelling and simple grammatical errors. These errors have been studied extensively and are relatively simple for humans. On…
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL
Longxu Dou, Yan Gao, Mingyang Pan +4
Existing text-to-SQL semantic parsers are typically designed for particular settings such as handling queries that span multiple tables, domains or turns which makes them ineffecti…
Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging
Yutai Hou, Cheng Chen, Xianzhen Luo +2
Prompting methods recently achieve impressive success in few-shot learning. These methods modify input samples with prompt sentence pieces, and decode label tokens to map samples t…
InterHT: Knowledge Graph Embeddings by Interaction between Head and Tail Entities
Baoxin Wang, Qingye Meng, Ziyue Wang +6
Knowledge graph embedding (KGE) models learn the representation of entities and relations in knowledge graphs. Distance-based methods show promising performance on link prediction…
Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding
Xiao Xu, Libo Qin, Kaiji Chen +3
Current researches on spoken language understanding (SLU) heavily are limited to a simple setting: the plain text-based SLU that takes the user utterance as input and generates its…
TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack
Zhen Yu, Xiaosen Wang, Wanxiang Che +1
Existing textual adversarial attacks usually utilize the gradient or prediction confidence to generate adversarial examples, making it hard to be deployed in real-world application…