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Exploring Equation as a Better Intermediate Meaning Representation for Numerical Reasoning
Dingzirui Wang, Longxu Dou, Wenbin Zhang +2
Numerical reasoning is vital for natural language processing models to understand and process numerical information in real-world scenarios. Most current methods first generate the…
MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System
Libo Qin, Shijue Huang, Qiguang Chen +5
Multi-modal sarcasm detection has attracted much recent attention. Nevertheless, the existing benchmark (MMSD) has some shortcomings that hinder the development of reliable multi-m…
MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification
Hongyuan Dong, Weinan Zhang, Wanxiang Che
Prompting methods have shown impressive performance in a variety of text mining tasks and applications, especially few-shot ones. Despite the promising prospects, the performance o…
OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding
Libo Qin, Qiguang Chen, Xiao Xu +2
Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e.g., intents and…
CSED: A Chinese Semantic Error Diagnosis Corpus
Bo Sun, Baoxin Wang, Yixuan Wang +4
Recently, much Chinese text error correction work has focused on Chinese Spelling Check (CSC) and Chinese Grammatical Error Diagnosis (CGED). In contrast, little attention has been…
Controllable Data Augmentation for Context-Dependent Text-to-SQL
Dingzirui Wang, Longxu Dou, Wanxiang Che
The limited scale of annotated data constraints existing context-dependent text-to-SQL models because of the complexity of labeling. The data augmentation method is a commonly used…
A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding
Wenbo Pan, Qiguang Chen, Xiao Xu +2
Zero-shot dialogue understanding aims to enable dialogue to track the user's needs without any training data, which has gained increasing attention. In this work, we investigate th…
A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification
Yunlong Feng, Bohan Li, Libo Qin +2
Cross-domain text classification aims to adapt models to a target domain that lacks labeled data. It leverages or reuses rich labeled data from the different but related source dom…
MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning
Bohan Li, Longxu Dou, Yutai Hou +5
Prompt-based learning has shown considerable promise in reformulating various downstream tasks as cloze problems by combining original input with a predetermined template. This app…
HIT-SCIR at MMNLU-22: Consistency Regularization for Multilingual Spoken Language Understanding
Bo Zheng, Zhouyang Li, Fuxuan Wei +3
Multilingual spoken language understanding (SLU) consists of two sub-tasks, namely intent detection and slot filling. To improve the performance of these two sub-tasks, we propose…
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge
Longxu Dou, Yan Gao, Xuqi Liu +6
In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific table…
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…