Search Results for author: Wanxiang Che

Found 156 papers, 81 papers with code

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.

Simple and Effective Graph-to-Graph Annotation Conversion

1 code implementation COLING 2022 Yuxuan Wang, Zhilin Lei, Yuqiu Ji, Wanxiang Che

Annotation conversion is an effective way to construct datasets under new annotation guidelines based on existing datasets with little human labour.

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

AutoCAP: Towards Automatic Cross-lingual Alignment Planning for Zero-shot Chain-of-Thought

1 code implementation20 Jun 2024 Yongheng Zhang, Qiguang Chen, Min Li, Wanxiang Che, Libo Qin

Cross-lingual chain-of-thought can effectively complete reasoning tasks across languages, which gains increasing attention.

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

Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey

no code implementations12 Jun 2024 Hao Yang, Yanyan Zhao, Yang Wu, Shilong Wang, Tian Zheng, Hongbo Zhang, Wanxiang Che, Bing Qin

Compared to traditional sentiment analysis, which only considers text, multimodal sentiment analysis needs to consider emotional signals from multimodal sources simultaneously and is therefore more consistent with the way how humans process sentiment in real-world scenarios.

Multimodal Sentiment Analysis

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

Against The Achilles' Heel: A Survey on Red Teaming for Generative Models

no code implementations31 Mar 2024 Lizhi Lin, Honglin Mu, Zenan Zhai, Minghan Wang, Yuxia Wang, Renxi Wang, Junjie Gao, Yixuan Zhang, Wanxiang Che, Timothy Baldwin, Xudong Han, Haonan Li

Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safety issues as various vulnerabilities are exposed.

LM-Combiner: A Contextual Rewriting Model for Chinese Grammatical Error Correction

no code implementations26 Mar 2024 YiXuan Wang, Baoxin Wang, Yijun Liu, Dayong Wu, Wanxiang Che

In this light, we propose the LM-Combiner, a rewriting model that can directly modify the over-correction of GEC system outputs without a model ensemble.

Grammatical Error Correction

Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants' API Invocation Capabilities

1 code implementation17 Mar 2024 Honglin Mu, Yang Xu, Yunlong Feng, Xiaofeng Han, Yitong Li, Yutai Hou, Wanxiang Che

With the rise of Large Language Models (LLMs), AI assistants' ability to utilize tools, especially through API calls, has advanced notably.

Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models

no code implementations1 Mar 2024 Xianzhen Luo, Qingfu Zhu, Zhiming Zhang, Xu Wang, Qing Yang, Dongliang Xu, Wanxiang Che

Presently, two dominant paradigms for collecting tuning data are natural-instruct (human-written) and self-instruct (automatically generated).

Program Synthesis

OneBit: Towards Extremely Low-bit Large Language Models

1 code implementation17 Feb 2024 Yuzhuang Xu, Xu Han, Zonghan Yang, Shuo Wang, Qingfu Zhu, Zhiyuan Liu, Weidong Liu, Wanxiang Che

Model quantification uses low bit-width values to represent the weight matrices of existing models to be quantized, which is a promising approach to reduce both storage and computational overheads of deploying highly anticipated LLMs.


Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL

1 code implementation16 Feb 2024 Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

Currently, the in-context learning method based on large language models (LLMs) has become the mainstream of text-to-SQL research.

In-Context Learning Text-To-SQL

Multi-Hop Table Retrieval for Open-Domain Text-to-SQL

no code implementations16 Feb 2024 Xuanliang Zhang, Dingzirui Wang, Longxu Dou, Qingfu Zhu, Wanxiang Che

To reduce the effect of the similar irrelevant entity, our method focuses on unretrieved entities at each hop and considers the low-ranked tables by beam search.

Table Retrieval Text-To-SQL

Exploring Hybrid Question Answering via Program-based Prompting

no code implementations16 Feb 2024 Qi Shi, Han Cui, Haofeng Wang, Qingfu Zhu, Wanxiang Che, Ting Liu

Question answering over heterogeneous data requires reasoning over diverse sources of data, which is challenging due to the large scale of information and organic coupling of heterogeneous data.

Code Generation Question Answering

Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes

no code implementations16 Feb 2024 Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

Numerical reasoning is an essential ability for NLP systems to handle numeric information.

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.

A Survey of Table Reasoning with Large Language Models

1 code implementation13 Feb 2024 Xuanliang Zhang, Dingzirui Wang, Longxu Dou, Qingfu Zhu, Wanxiang Che

In this paper, we analyze the mainstream techniques used to improve table reasoning performance in the LLM era, and the advantages of LLMs compared to pre-LLMs for solving table reasoning.

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

SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models

no code implementations16 Jan 2024 Weixiang Zhao, Shilong Wang, Yulin Hu, Yanyan Zhao, Bing Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che

Existing methods devise the learning module to acquire task-specific knowledge with parameter-efficient tuning (PET) block and the selection module to pick out the corresponding one for the testing input, aiming at handling the challenges of catastrophic forgetting and knowledge transfer in CL.

Continual Learning Transfer Learning

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.

Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue

1 code implementation23 Oct 2023 Yuanxing Liu, Wei-Nan Zhang, Yifan Chen, Yuchi Zhang, Haopeng Bai, Fan Feng, Hengbin Cui, Yongbin Li, Wanxiang Che

This paper investigates the effectiveness of combining LLM and CRS in E-commerce pre-sales dialogues, proposing two collaboration methods: CRS assisting LLM and LLM assisting CRS.

Language Modelling Large Language Model +1

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.

Exploring Equation as a Better Intermediate Meaning Representation for Numerical Reasoning

1 code implementation21 Aug 2023 Dingzirui Wang, Longxu Dou, Wenbin Zhang, Junyu Zeng, Wanxiang Che

So in this paper, we try to use equations as IMRs to solve the numerical reasoning task by addressing two problems: (1) Theoretically, how to prove that the equation is an IMR with higher generation accuracy than programs; (2) Empirically, how to improve the generation accuracy of equations with LLMs.


MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification

1 code implementation15 Jun 2023 Hongyuan Dong, Weinan Zhang, Wanxiang Che

Despite the promising prospects, the performance of prompting model largely depends on the design of prompt template and verbalizer.

Few-Shot Text Classification text-classification

ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning

1 code implementation31 May 2023 Xiao Xu, Bei Li, Chenfei Wu, Shao-Yen Tseng, Anahita Bhiwandiwalla, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan

With only 4M VLP data, ManagerTower achieves superior performances on various downstream VL tasks, especially 79. 15% accuracy on VQAv2 Test-Std, 86. 56% IR@1 and 95. 64% TR@1 on Flickr30K.

Representation Learning

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

CSED: A Chinese Semantic Error Diagnosis Corpus

no code implementations9 May 2023 Bo Sun, Baoxin Wang, YiXuan Wang, Wanxiang Che, Dayong Wu, Shijin Wang, Ting Liu

Our experiments show that powerful pre-trained models perform poorly on this corpus.

Controllable Data Augmentation for Context-Dependent Text-to-SQL

no code implementations27 Apr 2023 Dingzirui Wang, Longxu Dou, Wanxiang Che

In this paper, we introduce ConDA, which generates interactive questions and corresponding SQL results.

Data Augmentation Text-To-SQL

MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning

no code implementations19 Apr 2023 Bohan Li, Longxu Dou, Yutai Hou, Yunlong Feng, Honglin Mu, Qingfu Zhu, Qinghua Sun, Wanxiang Che

Prompt-based learning has shown considerable promise in reformulating various downstream tasks as cloze problems by combining original input with a predetermined template.

Data Augmentation Few-Shot Learning +1

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

Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification

no code implementations4 Feb 2023 Bohan Li, Xiao Xu, Xinghao Wang, Yutai Hou, Yunlong Feng, Feng Wang, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

In contrast, generative methods bring more image diversity in the augmented images but may not preserve semantic consistency, thus incorrectly changing the essential semantics of the original image.

Image Augmentation Image Classification +1

Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge

1 code implementation3 Jan 2023 Longxu Dou, Yan Gao, Xuqi Liu, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Min-Yen Kan, Jian-Guang Lou

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 tables.

Semantic Parsing Text-To-SQL

A Survey on Table-and-Text HybridQA: Concepts, Methods, Challenges and Future Directions

no code implementations27 Dec 2022 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.

Question Answering

MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing

1 code implementation27 Dec 2022 Longxu Dou, Yan Gao, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Jian-Guang Lou

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 interaction systems.

Benchmarking Semantic Parsing +1

A Survey on Natural Language Processing for Programming

no code implementations12 Dec 2022 Qingfu Zhu, Xianzhen Luo, Fang Liu, Cuiyun Gao, Wanxiang Che

Natural language processing for programming aims to use NLP techniques to assist programming.

LERT: A Linguistically-motivated Pre-trained Language Model

1 code implementation10 Nov 2022 Yiming Cui, Wanxiang Che, Shijin Wang, Ting Liu

We propose LERT, a pre-trained language model that is trained on three types of linguistic features along with the original MLM pre-training task, using a linguistically-informed pre-training (LIP) strategy.

Language Modelling Stock Market Prediction +1

MetaPrompting: Learning to Learn Better Prompts

1 code implementation COLING 2022 Yutai Hou, Hongyuan Dong, Xinghao Wang, Bohan Li, Wanxiang Che

Prompting method is regarded as one of the crucial progress for few-shot nature language processing.


Overview of CTC 2021: Chinese Text Correction for Native Speakers

1 code implementation11 Aug 2022 Honghong Zhao, Baoxin Wang, Dayong Wu, Wanxiang Che, Zhigang Chen, Shijin Wang

In this paper, we present an overview of the CTC 2021, a Chinese text correction task for native speakers.

BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning

1 code implementation17 Jun 2022 Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan

Vision-Language (VL) models with the Two-Tower architecture have dominated visual-language representation learning in recent years.

Representation Learning

Language Anisotropic Cross-Lingual Model Editing

1 code implementation25 May 2022 Yang Xu, Yutai Hou, Wanxiang Che, Min Zhang

On the newly defined cross-lingual model editing task, we empirically demonstrate the failure of monolingual baselines in propagating the edit to multiple languages and the effectiveness of the proposed language anisotropic model editing.

Model Editing

UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL

1 code implementation15 Mar 2022 Longxu Dou, Yan Gao, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Jian-Guang Lou

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 ineffective when applied to different settings.

Language Modelling Text-To-SQL

TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack

1 code implementation20 Jan 2022 Zhen Yu, Xiaosen Wang, Wanxiang Che, Kun He

Existing textual adversarial attacks usually utilize the gradient or prediction confidence to generate adversarial examples, making it hard to be deployed in real-world applications.

Adversarial Attack Hard-label Attack +3

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

Data Augmentation Approaches in Natural Language Processing: A Survey

1 code implementation5 Oct 2021 Bohan Li, Yutai Hou, Wanxiang Che

One of the main focuses of the DA methods is to improve the diversity of training data, thereby helping the model to better generalize to unseen testing data.

Data Augmentation

Discovering Drug-Target Interaction Knowledge from Biomedical Literature

no code implementations27 Sep 2021 Yutai Hou, Yingce Xia, Lijun Wu, Shufang Xie, Yang Fan, Jinhua Zhu, Wanxiang Che, Tao Qin, Tie-Yan Liu

We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations.

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

DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation

1 code implementation EMNLP 2021 Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che

In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2. 0) to enable researchers to explore a challenging task of multilingual and cross-lingual conversational recommendation.

Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training

2 code implementations EMNLP 2021 Bo Zheng, Li Dong, Shaohan Huang, Saksham Singhal, Wanxiang Che, Ting Liu, Xia Song, Furu Wei

We find that many languages are under-represented in recent cross-lingual language models due to the limited vocabulary capacity.

Language Modelling

Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models

no code implementations26 Aug 2021 Yiming Cui, Wei-Nan Zhang, Wanxiang Che, Ting Liu, Zhigang Chen, Shijin Wang

Achieving human-level performance on some of the Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs).

Machine Reading Comprehension Question Answering +1

Discovering Dialog Structure Graph for Coherent Dialog Generation

no code implementations ACL 2021 Jun Xu, Zeyang Lei, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che

Learning discrete dialog structure graph from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation.

Graph Neural Network Management

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling

no code implementations Findings (ACL) 2021 Yutai Hou, Yongkui Lai, Cheng Chen, Wanxiang Che, Ting Liu

However, dialogue language understanding contains two closely related tasks, i. e., intent detection and slot filling, and often benefits from jointly learning the two tasks.

Few-Shot Learning Intent Detection +2

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

1 code implementation10 May 2021 Yiming Cui, Ting Liu, Wanxiang Che, Zhigang Chen, Shijin Wang

Achieving human-level performance on some of Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs).

Machine Reading Comprehension Multi-Choice MRC +2

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

Discovering Dialog Structure Graph for Open-Domain Dialog Generation

no code implementations31 Dec 2020 Jun Xu, Zeyang Lei, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu

Learning interpretable dialog structure from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation.

Graph Neural Network Open-Domain Dialog

LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding

5 code implementations ACL 2021 Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou

Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents.

Document Image Classification Document Layout Analysis +6

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

C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling

1 code implementation13 Dec 2020 Yutai Hou, Sanyuan Chen, Wanxiang Che, Cheng Chen, Ting Liu

Slot filling, a fundamental module of spoken language understanding, often suffers from insufficient quantity and diversity of training data.

Data Augmentation slot-filling +2

HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser

no code implementations CONLL 2020 Longxu Dou, Yunlong Feng, Yuqiu Ji, Wanxiang Che, Ting Liu

This paper describes our submission system (HIT-SCIR) for the CoNLL 2020 shared task: Cross-Framework and Cross-Lingual Meaning Representation Parsing.

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

Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation

no code implementations ACL 2020 Jun Xu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu

To address the challenge of policy learning in open-domain multi-turn conversation, we propose to represent prior information about dialog transitions as a graph and learn a graph grounded dialog policy, aimed at fostering a more coherent and controllable dialog.

Response Generation

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

Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension

1 code implementation ACL 2020 Bo Zheng, Haoyang Wen, Yaobo Liang, Nan Duan, Wanxiang Che, Daxin Jiang, Ming Zhou, Ting Liu

Natural Questions is a new challenging machine reading comprehension benchmark with two-grained answers, which are a long answer (typically a paragraph) and a short answer (one or more entities inside the long answer).

Graph Attention Machine Reading Comprehension +1

Towards Conversational Recommendation over Multi-Type Dialogs

2 code implementations ACL 2020 Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu

We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e. g., QA) to a recommendation dialog, taking into account user's interests and feedback.

Vocal Bursts Type Prediction

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

Revisiting Pre-Trained Models for Chinese Natural Language Processing

6 code implementations Findings of the Association for Computational Linguistics 2020 Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu

Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models.

Language Modelling Stock Market Prediction

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 Sentence Cloze Dataset for Chinese Machine Reading Comprehension

1 code implementation COLING 2020 Yiming Cui, Ting Liu, Ziqing Yang, Zhipeng Chen, Wentao Ma, Wanxiang Che, Shijin Wang, Guoping Hu

To add diversity in this area, in this paper, we propose a new task called Sentence Cloze-style Machine Reading Comprehension (SC-MRC).

Machine Reading Comprehension Sentence

Discriminative Sentence Modeling for Story Ending Prediction

no code implementations19 Dec 2019 Yiming Cui, Wanxiang Che, Wei-Nan Zhang, Ting Liu, Shijin Wang, Guoping Hu

Story Ending Prediction is a task that needs to select an appropriate ending for the given story, which requires the machine to understand the story and sometimes needs commonsense knowledge.

Cloze Test Sentence

Contextual Recurrent Units for Cloze-style Reading Comprehension

no code implementations14 Nov 2019 Yiming Cui, Wei-Nan Zhang, Wanxiang Che, Ting Liu, Zhipeng Chen, Shijin Wang, Guoping Hu

Recurrent Neural Networks (RNN) are known as powerful models for handling sequential data, and especially widely utilized in various natural language processing tasks.

Reading Comprehension Sentence +2

Improving Machine Reading Comprehension via Adversarial Training

no code implementations9 Nov 2019 Ziqing Yang, Yiming Cui, Wanxiang Che, Ting Liu, Shijin Wang, Guoping Hu

With virtual adversarial training (VAT), we explore the possibility of improving the RC models with semi-supervised learning and prove that examples from a different task are also beneficial.

General Classification Image Classification +3

Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing

1 code implementation IJCNLP 2019 Yuxuan Wang, Wanxiang Che, Jiang Guo, Yijia Liu, Ting Liu

In this approach, a linear transformation is learned from contextual word alignments to align the contextualized embeddings independently trained in different languages.

Dependency Parsing Language Modelling +2

A Corpus-free State2Seq User Simulator for Task-oriented Dialogue

1 code implementation10 Sep 2019 Yutai Hou, Meng Fang, Wanxiang Che, Ting Liu

The framework builds a user simulator by first generating diverse dialogue data from templates and then build a new State2Seq user simulator on the data.

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

Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency

1 code implementation ACL 2019 Shuhuai Ren, Yihe Deng, Kun He, Wanxiang Che

Experiments on three popular datasets using convolutional as well as LSTM models show that PWWS reduces the classification accuracy to the most extent, and keeps a very low word substitution rate.

Adversarial Attack General Classification +5

Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding

no code implementations20 Jun 2019 Yutai Hou, Zhihan Zhou, Yijia Liu, Ning Wang, Wanxiang Che, Han Liu, Ting Liu

It calculates emission score with similarity based methods and obtains transition score with a specially designed transfer mechanism.

Few-Shot Learning named-entity-recognition +3

Pre-Training with Whole Word Masking for Chinese BERT

2 code implementations19 Jun 2019 Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang

To demonstrate the effectiveness of these models, we create a series of Chinese pre-trained language models as our baselines, including BERT, RoBERTa, ELECTRA, RBT, etc.

Document Classification General Classification +5

An AMR Aligner Tuned by Transition-based Parser

1 code implementation EMNLP 2018 Yijia Liu, Wanxiang Che, Bo Zheng, Bing Qin, Ting Liu

In this paper, we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser.

AMR Parsing POS +1

Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding

1 code implementation COLING 2018 Yutai Hou, Yijia Liu, Wanxiang Che, Ting Liu

In this paper, we study the problem of data augmentation for language understanding in task-oriented dialogue system.

Text Augmentation

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

Deep Learning in Lexical Analysis and Parsing

no code implementations IJCNLP 2017 Wanxiang Che, Yue Zhang

Neural networks, also with a fancy name deep learning, just right can overcome the above {``}feature engineering{''} problem.

Dependency Parsing Feature Engineering +3

Transition-Based Disfluency Detection using LSTMs

1 code implementation EMNLP 2017 Shaolei Wang, Wanxiang Che, Yue Zhang, Meishan Zhang, Ting Liu

In this paper, we model the problem of disfluency detection using a transition-based framework, which incrementally constructs and labels the disfluency chunk of input sentences using a new transition system without syntax information.

Information Retrieval

The HIT-SCIR System for End-to-End Parsing of Universal Dependencies

no code implementations CONLL 2017 Wanxiang Che, Jiang Guo, Yuxuan Wang, Bo Zheng, Huaipeng Zhao, Yang Liu, Dechuan Teng, Ting Liu

Our system includes three pipelined components: \textit{tokenization}, \textit{Part-of-Speech} (POS) \textit{tagging} and \textit{dependency parsing}.

Dependency Parsing Information Retrieval +4

A Unified Architecture for Semantic Role Labeling and Relation Classification

no code implementations COLING 2016 Jiang Guo, Wanxiang Che, Haifeng Wang, Ting Liu, Jun Xu

This paper describes a unified neural architecture for identifying and classifying multi-typed semantic relations between words in a sentence.

Classification Feature Engineering +9

A Neural Attention Model for Disfluency Detection

no code implementations COLING 2016 Shaolei Wang, Wanxiang Che, Ting Liu

We treat disfluency detection as a sequence-to-sequence problem and propose a neural attention-based model which can efficiently model the long-range dependencies between words and make the resulting sentence more likely to be grammatically correct.

Decoder Information Retrieval +1