Search Results for author: Libo Qin

Found 46 papers, 31 papers with code

GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding

1 code implementation ACL 2022 Libo Qin, Qiguang Chen, Tianbao Xie, Qixin Li, Jian-Guang Lou, Wanxiang Che, Min-Yen Kan

Specifically, we employ contrastive learning, leveraging bilingual dictionaries to construct multilingual views of the same utterance, then encourage their representations to be more similar than negative example pairs, which achieves to explicitly align representations of similar sentences across languages.

Contrastive Learning Cross-Lingual Transfer +2

CGIM: A Cycle Guided Interactive Learning Model for Consistency Identification in Task-oriented Dialogue

1 code implementation COLING 2022 Libo Qin, Qiguang Chen, Tianbao Xie, Qian Liu, Shijue Huang, Wanxiang Che, Zhou Yu

Consistency identification in task-oriented dialog (CI-ToD) usually consists of three subtasks, aiming to identify inconsistency between current system response and current user response, dialog history and the corresponding knowledge base.

CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

no code implementations15 Jun 2024 Libo Qin, Fuxuan Wei, Qiguang Chen, Jingxuan Zhou, Shijue Huang, Jiasheng Si, Wenpeng Lu, Wanxiang Che

To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU.

Intent Detection slot-filling +2

M$^3$CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought

1 code implementation26 May 2024 Qiguang Chen, Libo Qin, Jin Zhang, Zhi Chen, Xiao Xu, Wanxiang Che

In addition, we highlight that the current VLLMs still struggle to correctly reason in M$^3$CoT and there remains a large gap between existing VLLMs and human performance in M$^3$CoT, despite their superior results on previous MCoT benchmarks.

Large Language Models Meet NLP: A Survey

1 code implementation21 May 2024 Libo Qin, Qiguang Chen, Xiachong Feng, Yang Wu, Yongheng Zhang, Yinghui Li, Min Li, Wanxiang Che, Philip S. Yu

While large language models (LLMs) like ChatGPT have shown impressive capabilities in Natural Language Processing (NLP) tasks, a systematic investigation of their potential in this field remains largely unexplored.

Improving Language Model Reasoning with Self-motivated Learning

no code implementations10 Apr 2024 Yunlong Feng, Yang Xu, Libo Qin, Yasheng Wang, Wanxiang Che

The framework motivates the model itself to automatically generate rationales on existing datasets.

Language Modelling

Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers

no code implementations7 Apr 2024 Libo Qin, Qiguang Chen, YuHang Zhou, Zhi Chen, Yinghui Li, Lizi Liao, Min Li, Wanxiang Che, Philip S. Yu

To this end, in this paper, we present a thorough review and provide a unified perspective to summarize the recent progress as well as emerging trends in multilingual large language models (MLLMs) literature.

Language Modelling Large Language Model

Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction

no code implementations18 Feb 2024 Yinghui Li, Shang Qin, Jingheng Ye, Shirong Ma, Yangning Li, Libo Qin, Xuming Hu, Wenhao Jiang, Hai-Tao Zheng, Philip S. Yu

To promote the CGEC field to better adapt to the era of LLMs, we rethink the roles of LLMs in the CGEC task so that they can be better utilized and explored in CGEC.

Grammatical Error Correction

Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts

no code implementations16 Feb 2024 Xianzhen Luo, Qingfu Zhu, Zhiming Zhang, Libo Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che

In this paper, we conduct comprehensive experiments on the programming languages used in PoT and find that no single language consistently delivers optimal performance across all tasks and models.

Pro-HAN: A Heterogeneous Graph Attention Network for Profile-Based Spoken Language Understanding

1 code implementation6 Feb 2024 Dechuan Teng, Chunlin Lu, Xiao Xu, Wanxiang Che, Libo Qin

Recently, Profile-based Spoken Language Understanding (SLU) has gained increasing attention, which aims to incorporate various types of supplementary profile information (i. e., Knowledge Graph, User Profile, Context Awareness) to eliminate the prevalent ambiguities in user utterances.

Graph Attention Spoken Language Understanding

SDIF-DA: A Shallow-to-Deep Interaction Framework with Data Augmentation for Multi-modal Intent Detection

1 code implementation31 Dec 2023 Shijue Huang, Libo Qin, Bingbing Wang, Geng Tu, Ruifeng Xu

The two core challenges for multi-modal intent detection are (1) how to effectively align and fuse different features of modalities and (2) the limited labeled multi-modal intent training data.

Data Augmentation Intent Detection +2

DTIAM: A unified framework for predicting drug-target interactions, binding affinities and activation/inhibition mechanisms

1 code implementation23 Dec 2023 Zhangli Lu, Chuqi Lei, Kaili Wang, Libo Qin, Jing Tang, Min Li

DTIAM, for the first time, provides a unified framework for accurate and robust prediction of drug-target interactions, binding affinities, and activation/inhibition mechanisms.

Drug Discovery

End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions

no code implementations15 Nov 2023 Libo Qin, Wenbo Pan, Qiguang Chen, Lizi Liao, Zhou Yu, Yue Zhang, Wanxiang Che, Min Li

End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity.

Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages

1 code implementation23 Oct 2023 Libo Qin, Qiguang Chen, Fuxuan Wei, Shijue Huang, Wanxiang Che

The cross-lingual alignment prompting is responsible for aligning representations across different languages, whereas the task-specific solver prompting is used to generate the final chain of thoughts and results for the reasoning task.

Improving Few-shot and Zero-shot Entity Linking with Coarse-to-Fine Lexicon-based Retriever

no code implementations7 Aug 2023 Shijue Huang, Bingbing Wang, Libo Qin, Qin Zhao, Ruifeng Xu

Few-shot and zero-shot entity linking focus on the tail and emerging entities, which are more challenging but closer to real-world scenarios.

Entity Linking Retrieval

OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding

1 code implementation17 May 2023 Libo Qin, Qiguang Chen, Xiao Xu, Yunlong Feng, Wanxiang Che

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 slots).

Spoken Language Understanding

LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model

1 code implementation13 Apr 2023 Hao Fei, Shengqiong Wu, Jingye Li, Bobo Li, Fei Li, Libo Qin, Meishan Zhang, Min Zhang, Tat-Seng Chua

Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under a GLM.

Language Modelling UIE

A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding

no code implementations9 Apr 2023 Wenbo Pan, Qiguang Chen, Xiao Xu, Wanxiang Che, Libo Qin

Zero-shot dialogue understanding aims to enable dialogue to track the user's needs without any training data, which has gained increasing attention.

Dialogue State Tracking slot-filling +2

GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective

1 code implementation15 Nov 2022 Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, Yue Zhang

Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase.

Natural Language Understanding Out-of-Distribution Generalization

Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding

1 code implementation22 Dec 2021 Xiao Xu, Libo Qin, Kaiji Chen, Guoxing Wu, Linlin Li, Wanxiang Che

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 corresponding semantic frames (e. g., intent and slots).

Intent Detection Semantic Frame Parsing +4

NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

2 code implementations6 Dec 2021 Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahendiran, Simon Mille, Ashish Shrivastava, Samson Tan, Tongshuang Wu, Jascha Sohl-Dickstein, Jinho D. Choi, Eduard Hovy, Ondrej Dusek, Sebastian Ruder, Sajant Anand, Nagender Aneja, Rabin Banjade, Lisa Barthe, Hanna Behnke, Ian Berlot-Attwell, Connor Boyle, Caroline Brun, Marco Antonio Sobrevilla Cabezudo, Samuel Cahyawijaya, Emile Chapuis, Wanxiang Che, Mukund Choudhary, Christian Clauss, Pierre Colombo, Filip Cornell, Gautier Dagan, Mayukh Das, Tanay Dixit, Thomas Dopierre, Paul-Alexis Dray, Suchitra Dubey, Tatiana Ekeinhor, Marco Di Giovanni, Tanya Goyal, Rishabh Gupta, Louanes Hamla, Sang Han, Fabrice Harel-Canada, Antoine Honore, Ishan Jindal, Przemyslaw K. Joniak, Denis Kleyko, Venelin Kovatchev, Kalpesh Krishna, Ashutosh Kumar, Stefan Langer, Seungjae Ryan Lee, Corey James Levinson, Hualou Liang, Kaizhao Liang, Zhexiong Liu, Andrey Lukyanenko, Vukosi Marivate, Gerard de Melo, Simon Meoni, Maxime Meyer, Afnan Mir, Nafise Sadat Moosavi, Niklas Muennighoff, Timothy Sum Hon Mun, Kenton Murray, Marcin Namysl, Maria Obedkova, Priti Oli, Nivranshu Pasricha, Jan Pfister, Richard Plant, Vinay Prabhu, Vasile Pais, Libo Qin, Shahab Raji, Pawan Kumar Rajpoot, Vikas Raunak, Roy Rinberg, Nicolas Roberts, Juan Diego Rodriguez, Claude Roux, Vasconcellos P. H. S., Ananya B. Sai, Robin M. Schmidt, Thomas Scialom, Tshephisho Sefara, Saqib N. Shamsi, Xudong Shen, Haoyue Shi, Yiwen Shi, Anna Shvets, Nick Siegel, Damien Sileo, Jamie Simon, Chandan Singh, Roman Sitelew, Priyank Soni, Taylor Sorensen, William Soto, Aman Srivastava, KV Aditya Srivatsa, Tony Sun, Mukund Varma T, A Tabassum, Fiona Anting Tan, Ryan Teehan, Mo Tiwari, Marie Tolkiehn, Athena Wang, Zijian Wang, Gloria Wang, Zijie J. Wang, Fuxuan Wei, Bryan Wilie, Genta Indra Winata, Xinyi Wu, Witold Wydmański, Tianbao Xie, Usama Yaseen, Michael A. Yee, Jing Zhang, Yue Zhang

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.

Data Augmentation

Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

1 code implementation23 Sep 2021 Libo Qin, Tianbao Xie, Shijue Huang, Qiguang Chen, Xiao Xu, Wanxiang Che

Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation.

Benchmarking Response Generation

FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark

1 code implementation15 Jul 2021 Liang Xu, Xiaojing Lu, Chenyang Yuan, Xuanwei Zhang, Huilin Xu, Hu Yuan, Guoao Wei, Xiang Pan, Xin Tian, Libo Qin, Hu Hai

While different learning schemes -- fine-tuning, zero-shot, and few-shot learning -- have been widely explored and compared for languages such as English, there is comparatively little work in Chinese to fairly and comprehensively evaluate and compare these methods and thus hinders cumulative progress.

Few-Shot Learning Machine Reading Comprehension +4

Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization

1 code implementation ACL 2021 Xiachong Feng, Xiaocheng Feng, Libo Qin, Bing Qin, Ting Liu

Current dialogue summarization systems usually encode the text with a number of general semantic features (e. g., keywords and topics) to gain more powerful dialogue modeling capabilities.

Conversational Response Generation Language Modelling +1

A Survey on Spoken Language Understanding: Recent Advances and New Frontiers

1 code implementation4 Mar 2021 Libo Qin, Tianbao Xie, Wanxiang Che, Ting Liu

Spoken Language Understanding (SLU) aims to extract the semantics frame of user queries, which is a core component in a task-oriented dialog system.

Spoken Language Understanding

Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification

1 code implementation24 Dec 2020 Libo Qin, Zhouyang Li, Wanxiang Che, Minheng Ni, Ting Liu

The dialog context information (contextual information) and the mutual interaction information are two key factors that contribute to the two related tasks.

Graph Attention Sentiment Analysis +1

A Co-Interactive Transformer for Joint Slot Filling and Intent Detection

1 code implementation8 Oct 2020 Libo Qin, Tailu Liu, Wanxiang Che, Bingbing Kang, Sendong Zhao, Ting Liu

Instead of adopting the self-attention mechanism in vanilla Transformer, we propose a co-interactive module to consider the cross-impact by building a bidirectional connection between the two related tasks.

Intent Detection slot-filling +2

N-LTP: An Open-source Neural Language Technology Platform for Chinese

1 code implementation EMNLP (ACL) 2021 Wanxiang Che, Yunlong Feng, Libo Qin, Ting Liu

We introduce \texttt{N-LTP}, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: {lexical analysis} (Chinese word segmentation, part-of-speech tagging, and named entity recognition), {syntactic parsing} (dependency parsing), and {semantic parsing} (semantic dependency parsing and semantic role labeling).

Chinese Word Segmentation Dependency Parsing +8

DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification

no code implementations16 Aug 2020 Libo Qin, Wanxiang Che, Yangming Li, Minheng Ni, Ting Liu

In dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers intentions, where dialog act and sentiment can indicate the explicit and the implicit intentions separately.

Relation Relation Network +2

Dialogue State Induction Using Neural Latent Variable Models

1 code implementation13 Aug 2020 Qingkai Min, Libo Qin, Zhiyang Teng, Xiao Liu, Yue Zhang

Dialogue state modules are a useful component in a task-oriented dialogue system.

CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP

1 code implementation11 Jun 2020 Libo Qin, Minheng Ni, Yue Zhang, Wanxiang Che

Compared with the existing work, our method does not rely on bilingual sentences for training, and requires only one training process for multiple target languages.

Data Augmentation

Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware Parameterization

no code implementations30 Apr 2020 Libo Qin, Minheng Ni, Yue Zhang, Wanxiang Che, Yangming Li, Ting Liu

Spoken language understanding has been addressed as a supervised learning problem, where a set of training data is available for each domain.

Spoken Language Understanding

Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog

1 code implementation ACL 2020 Libo Qin, Xiao Xu, Wanxiang Che, Yue Zhang, Ting Liu

However, there has been relatively little research on how to effectively use data from all domains to improve the performance of each domain and also unseen domains.

Scheduling Task-Oriented Dialogue Systems

AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling

1 code implementation Findings of the Association for Computational Linguistics 2020 Libo Qin, Xiao Xu, Wanxiang Che, Ting Liu

Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information, making a fine-grained intent information integration for the token-level slot prediction.

Intent Detection slot-filling +2

A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding

2 code implementations IJCNLP 2019 Libo Qin, Wanxiang Che, Yangming Li, Haoyang Wen, Ting Liu

In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge.

Intent Detection slot-filling +2

Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation

no code implementations COLING 2018 Haoyang Wen, Yijia Liu, Wanxiang Che, Libo Qin, Ting Liu

Classic pipeline models for task-oriented dialogue system require explicit modeling the dialogue states and hand-crafted action spaces to query a domain-specific knowledge base.

Task-Oriented Dialogue Systems

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