Search Results for author: Xiaojun Quan

Found 44 papers, 29 papers with code

Learning to Answer Psychological Questionnaire for Personality Detection

no code implementations Findings (EMNLP) 2021 Feifan Yang, Tao Yang, Xiaojun Quan, Qinliang Su

We argue that the posts created by a user contain critical contents that could help answer the questions in a questionnaire, resulting in an assessment of his personality by linking the texts and the questionnaire.

RoleInteract: Evaluating the Social Interaction of Role-Playing Agents

1 code implementation20 Mar 2024 Hongzhan Chen, Hehong Chen, Ming Yan, Wenshen Xu, Xing Gao, Weizhou Shen, Xiaojun Quan, Chenliang Li, Ji Zhang, Fei Huang, Jingren Zhou

In this paper, we introduce RoleInteract, the first benchmark designed to systematically evaluate the sociality of role-playing conversational agents at both individual and group levels of social interactions.

FuseChat: Knowledge Fusion of Chat Models

1 code implementation25 Feb 2024 Fanqi Wan, ZiYi Yang, Longguang Zhong, Xiaojun Quan, Xinting Huang, Wei Bi

Recently, \textsc{FuseLLM} introduced the concept of knowledge fusion to transfer the collective knowledge of multiple structurally varied LLMs into a target LLM through lightweight continual training.

Alirector: Alignment-Enhanced Chinese Grammatical Error Corrector

no code implementations7 Feb 2024 Haihui Yang, Xiaojun Quan

Then, we combine the source sentence with the initial correction and feed it through an alignment model for another round of correction, aiming to enforce the alignment model to focus on potential overcorrection.

Grammatical Error Correction Sentence

Knowledge Fusion of Large Language Models

1 code implementation19 Jan 2024 Fanqi Wan, Xinting Huang, Deng Cai, Xiaojun Quan, Wei Bi, Shuming Shi

In this paper, we introduce the notion of knowledge fusion for LLMs, aimed at combining the capabilities of existing LLMs and transferring them into a single LLM.

Code Generation

Knowledge Verification to Nip Hallucination in the Bud

1 code implementation19 Jan 2024 Fanqi Wan, Xinting Huang, Leyang Cui, Xiaojun Quan, Wei Bi, Shuming Shi

While large language models (LLMs) have demonstrated exceptional performance across various tasks following human alignment, they may still generate responses that sound plausible but contradict factual knowledge, a phenomenon known as \emph{hallucination}.

Hallucination World Knowledge

Small LLMs Are Weak Tool Learners: A Multi-LLM Agent

1 code implementation14 Jan 2024 Weizhou Shen, Chenliang Li, Hongzhan Chen, Ming Yan, Xiaojun Quan, Hehong Chen, Ji Zhang, Fei Huang

Each component is implemented by a single LLM that focuses on a specific capability and collaborates with others to accomplish the task.

Language Modelling Large Language Model

Knowledge Distillation for Closed-Source Language Models

no code implementations13 Jan 2024 Hongzhan Chen, Xiaojun Quan, Hehong Chen, Ming Yan, Ji Zhang

The prior estimation aims to derive a prior distribution by utilizing the corpus generated by closed-source language models, while the posterior estimation employs a proxy model to update the prior distribution and derive a posterior distribution.

Knowledge Distillation

PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection

1 code implementation31 Oct 2023 Tao Yang, Tianyuan Shi, Fanqi Wan, Xiaojun Quan, Qifan Wang, Bingzhe Wu, Jiaxiang Wu

Drawing inspiration from Psychological Questionnaires, which are carefully designed by psychologists to evaluate individual personality traits through a series of targeted items, we argue that these items can be regarded as a collection of well-structured chain-of-thought (CoT) processes.

Dual-Feedback Knowledge Retrieval for Task-Oriented Dialogue Systems

no code implementations23 Oct 2023 Tianyuan Shi, Liangzhi Li, Zijian Lin, Tao Yang, Xiaojun Quan, Qifan Wang

Efficient knowledge retrieval plays a pivotal role in ensuring the success of end-to-end task-oriented dialogue systems by facilitating the selection of relevant information necessary to fulfill user requests.

Open-Domain Question Answering Response Generation +2

MCC-KD: Multi-CoT Consistent Knowledge Distillation

1 code implementation23 Oct 2023 Hongzhan Chen, Siyue Wu, Xiaojun Quan, Rui Wang, Ming Yan, Ji Zhang

Large language models (LLMs) have showcased remarkable capabilities in complex reasoning through chain of thought (CoT) prompting.

Knowledge Distillation Mathematical Reasoning

Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration

1 code implementation13 Oct 2023 Fanqi Wan, Xinting Huang, Tao Yang, Xiaojun Quan, Wei Bi, Shuming Shi

Instruction-tuning can be substantially optimized through enhanced diversity, resulting in models capable of handling a broader spectrum of tasks.

Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System

1 code implementation13 Oct 2023 Weizhou Shen, Yingqi Gao, Canbin Huang, Fanqi Wan, Xiaojun Quan, Wei Bi

The results demonstrate that when combined with meta knowledge, the response generator can effectively leverage high-quality knowledge records from the retriever and enhance the quality of generated responses.

Response Generation Retrieval +1

Disentangled Phonetic Representation for Chinese Spelling Correction

1 code implementation24 May 2023 Zihong Liang, Xiaojun Quan, Qifan Wang

Chinese Spelling Correction (CSC) aims to detect and correct erroneous characters in Chinese texts.

Spelling Correction

AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression

1 code implementation17 May 2023 Siyue Wu, Hongzhan Chen, Xiaojun Quan, Qifan Wang, Rui Wang

To enhance the knowledge transfer of model reasoning and generalization, we further explore multi-view attribution distillation on all potential decisions of the teacher.

Knowledge Distillation Language Modelling +2

Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog

1 code implementation17 May 2023 Fanqi Wan, Weizhou Shen, Ke Yang, Xiaojun Quan, Wei Bi

Retrieving proper domain knowledge from an external database lies at the heart of end-to-end task-oriented dialog systems to generate informative responses.

Attribute Response Generation +1

Generic Dependency Modeling for Multi-Party Conversation

1 code implementation21 Feb 2023 Weizhou Shen, Xiaojun Quan, Ke Yang

To model the dependencies between utterances in multi-party conversations, we propose a simple and generic framework based on the dependency parsing results of utterances.

Dependency Parsing

Orders Are Unwanted: Dynamic Deep Graph Convolutional Network for Personality Detection

1 code implementation3 Dec 2022 Tao Yang, Jinghao Deng, Xiaojun Quan, Qifan Wang

Predicting personality traits based on online posts has emerged as an important task in many fields such as social network analysis.

AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning

1 code implementation12 Oct 2022 Tao Yang, Jinghao Deng, Xiaojun Quan, Qifan Wang, Shaoliang Nie

Fine-tuning large pre-trained language models on downstream tasks is apt to suffer from overfitting when limited training data is available.

Language Modelling

XPrompt: Exploring the Extreme of Prompt Tuning

no code implementations10 Oct 2022 Fang Ma, Chen Zhang, Lei Ren, Jingang Wang, Qifan Wang, Wei Wu, Xiaojun Quan, Dawei Song

Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for performing downstream tasks in a parameter-efficient manner.

UBARv2: Towards Mitigating Exposure Bias in Task-Oriented Dialogs

1 code implementation15 Sep 2022 Yunyi Yang, Hong Ding, Qingyi Liu, Xiaojun Quan

This paper studies the exposure bias problem in task-oriented dialog systems, where the model's generated content over multiple turns drives the dialog context away from the ground-truth distribution at training time, introducing error propagation and damaging the robustness of the TOD system.

Joint Generator-Ranker Learning for Natural Language Generation

2 code implementations28 Jun 2022 Weizhou Shen, Yeyun Gong, Yelong Shen, Song Wang, Xiaojun Quan, Nan Duan, Weizhu Chen

Generate-then-rank is a widely used mechanism for text generation, where a generator produces multiple text candidates and a ranker chooses the best one among the text candidates.

Question Generation Question-Generation +2

GL-RG: Global-Local Representation Granularity for Video Captioning

1 code implementation22 May 2022 Liqi Yan, Qifan Wang, Yiming Cui, Fuli Feng, Xiaojun Quan, Xiangyu Zhang, Dongfang Liu

Video captioning is a challenging task as it needs to accurately transform visual understanding into natural language description.

Caption Generation Descriptive +1

Deep Partial Multiplex Network Embedding

no code implementations5 Mar 2022 Qifan Wang, Yi Fang, Anirudh Ravula, Ruining He, Bin Shen, Jingang Wang, Xiaojun Quan, Dongfang Liu

Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks.

Link Prediction Network Embedding +1

WebFormer: The Web-page Transformer for Structure Information Extraction

no code implementations1 Feb 2022 Qifan Wang, Yi Fang, Anirudh Ravula, Fuli Feng, Xiaojun Quan, Dongfang Liu

Structure information extraction refers to the task of extracting structured text fields from web pages, such as extracting a product offer from a shopping page including product title, description, brand and price.

Deep Attention document understanding +1

Psycholinguistic Tripartite Graph Network for Personality Detection

no code implementations ACL 2021 Tao Yang, Feifan Yang, Haolan Ouyang, Xiaojun Quan

In this paper, we propose a psycholinguistic knowledge-based tripartite graph network, TrigNet, which consists of a tripartite graph network and a BERT-based graph initializer.

Graph Attention Graph Learning

Bi-Granularity Contrastive Learning for Post-Training in Few-Shot Scene

no code implementations Findings (ACL) 2021 Ruikun Luo, Guanhuan Huang, Xiaojun Quan

The major paradigm of applying a pre-trained language model to downstream tasks is to fine-tune it on labeled task data, which often suffers instability and low performance when the labeled examples are scarce.~One way to alleviate this problem is to apply post-training on unlabeled task data before fine-tuning, adapting the pre-trained model to target domains by contrastive learning that considers either token-level or sequence-level similarity.

Contrastive Learning Data Augmentation +2

Directed Acyclic Graph Network for Conversational Emotion Recognition

1 code implementation ACL 2021 Weizhou Shen, Siyue Wu, Yunyi Yang, Xiaojun Quan

In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea.

Emotion Recognition in Conversation

Syntax-Enhanced Pre-trained Model

1 code implementation ACL 2021 Zenan Xu, Daya Guo, Duyu Tang, Qinliang Su, Linjun Shou, Ming Gong, Wanjun Zhong, Xiaojun Quan, Nan Duan, Daxin Jiang

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa.

Entity Typing Question Answering +1

DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition

4 code implementations16 Dec 2020 Weizhou Shen, Junqing Chen, Xiaojun Quan, Zhixian Xie

Specifically, we first modify the recurrence mechanism of XLNet from segment-level to utterance-level in order to better model the conversational data.

Emotion Recognition in Conversation

UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2

1 code implementation7 Dec 2020 Yunyi Yang, Yunhao Li, Xiaojun Quan

This paper presents our task-oriented dialog system UBAR which models task-oriented dialogs on a dialog session level.

Language Modelling Response Generation

Multi-choice Relational Reasoning for Machine Reading Comprehension

no code implementations COLING 2020 Wuya Chen, Xiaojun Quan, Chunyu Kit, Zhengcheng Min, Jiahai Wang

We propose a multi-choice relational reasoning (McR$^2$) model with an aim to enable relational reasoning on candidates based on fusion representations of document, query and candidates.

Machine Reading Comprehension Relational Reasoning

Constituency Lattice Encoding for Aspect Term Extraction

1 code implementation COLING 2020 Yunyi Yang, Kun Li, Xiaojun Quan, Weizhou Shen, Qinliang Su

One of the remaining challenges for aspect term extraction in sentiment analysis resides in the extraction of phrase-level aspect terms, which is non-trivial to determine the boundaries of such terms.

Aspect Term Extraction and Sentiment Classification Sentence +1

Low-Resource Generation of Multi-hop Reasoning Questions

no code implementations ACL 2020 Jianxing Yu, Wei Liu, Shuang Qiu, Qinliang Su, Kai Wang, Xiaojun Quan, Jian Yin

Specifically, we first build a multi-hop generation model and guide it to satisfy the logical rationality by the reasoning chain extracted from a given text.

Machine Reading Comprehension valid

Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge

1 code implementation ACL 2020 Yuanhe Tian, Yan Song, Xiang Ao, Fei Xia, Xiaojun Quan, Tong Zhang, Yonggang Wang

Chinese word segmentation (CWS) and part-of-speech (POS) tagging are important fundamental tasks for Chinese language processing, where joint learning of them is an effective one-step solution for both tasks.

Chinese Word Segmentation Part-Of-Speech Tagging +2

Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation

no code implementations ACL 2020 Kun Li, Chengbo Chen, Xiaojun Quan, Qing Ling, Yan Song

In this paper, we formulate the data augmentation as a conditional generation task: generating a new sentence while preserving the original opinion targets and labels.

Data Augmentation Extract Aspect +3

Multi-Domain Dialogue Acts and Response Co-Generation

1 code implementation ACL 2020 Kai Wang, Junfeng Tian, Rui Wang, Xiaojun Quan, Jianxing Yu

Unlike those pipeline approaches, our act generation module preserves the semantic structures of multi-domain dialogue acts and our response generation module dynamically attends to different acts as needed.

Response Generation Task-Oriented Dialogue Systems

A Deep Neural Information Fusion Architecture for Textual Network Embeddings

no code implementations IJCNLP 2019 Zenan Xu, Qinliang Su, Xiaojun Quan, Weijia Zhang

Textual network embeddings aim to learn a low-dimensional representation for every node in the network so that both the structural and textual information from the networks can be well preserved in the representations.

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