Search Results for author: Xiaojiang Liu

Found 36 papers, 14 papers with code

Learning Bias-reduced Word Embeddings Using Dictionary Definitions

1 code implementation Findings (ACL) 2022 Haozhe An, Xiaojiang Liu, Donald Zhang

Pre-trained word embeddings, such as GloVe, have shown undesirable gender, racial, and religious biases.

Word Embeddings

Event Extraction as Machine Reading Comprehension

no code implementations EMNLP 2020 Jian Liu, Yubo Chen, Kang Liu, Wei Bi, Xiaojiang Liu

ii) Our model is excelled in the data-scarce scenario, for example, obtaining 49. 8{\%} in F1 for event argument extraction with only 1{\%} data, compared with 2. 2{\%} of the previous method.

Event Argument Extraction Event Extraction +5

Exploring Format Consistency for Instruction Tuning

1 code implementation28 Jul 2023 Shihao Liang, Runchu Tian, Kunlun Zhu, Yujia Qin, Huadong Wang, Xin Cong, Zhiyuan Liu, Xiaojiang Liu, Maosong Sun

Instruction tuning has emerged as a promising approach to enhancing large language models in following human instructions.


Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation

2 code implementations6 Jun 2022 Jin Xu, Xiaojiang Liu, Jianhao Yan, Deng Cai, Huayang Li, Jian Li

While large-scale neural language models, such as GPT2 and BART, have achieved impressive results on various text generation tasks, they tend to get stuck in undesirable sentence-level loops with maximization-based decoding algorithms (\textit{e. g.}, greedy search).

Sentence Text Generation +1

Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems

1 code implementation COLING 2020 Jian Wang, Junhao Liu, Wei Bi, Xiaojiang Liu, Kejing He, Ruifeng Xu, Min Yang

To conquer these limitations, we propose a Dual Dynamic Memory Network (DDMN) for multi-turn dialog generation, which maintains two core components: dialog memory manager and KB memory manager.

TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching

1 code implementation COLING 2020 Heng Gong, Yawei Sun, Xiaocheng Feng, Bing Qin, Wei Bi, Xiaojiang Liu, Ting Liu

Although neural table-to-text models have achieved remarkable progress with the help of large-scale datasets, they suffer insufficient learning problem with limited training data.

Few-Shot Learning Language Modelling +2

Profile Consistency Identification for Open-domain Dialogue Agents

1 code implementation EMNLP 2020 Haoyu Song, Yan Wang, Wei-Nan Zhang, Zhengyu Zhao, Ting Liu, Xiaojiang Liu

Maintaining a consistent attribute profile is crucial for dialogue agents to naturally converse with humans.


Enhancing Dialogue Generation via Multi-Level Contrastive Learning

no code implementations19 Sep 2020 Xin Li, Piji Li, Yan Wang, Xiaojiang Liu, Wai Lam

Most of the existing works for dialogue generation are data-driven models trained directly on corpora crawled from websites.

Contrastive Learning Dialogue Generation +1

Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport

no code implementations14 Aug 2020 Zelong Yang, Zhufeng Pan, Yan Wang, Deng Cai, Xiaojiang Liu, Shuming Shi, Shao-Lun Huang

With the rapid prevalence and explosive development of MOBA esports (Multiplayer Online Battle Arena electronic sports), much research effort has been devoted to automatically predicting game results (win predictions).


Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation

no code implementations ACL 2020 Zhiliang Tian, Wei Bi, Dongkyu Lee, Lanqing Xue, Yiping Song, Xiaojiang Liu, Nevin L. Zhang

In previous work, the external document is utilized by (1) creating a context-aware document memory that integrates information from the document and the conversational context, and then (2) generating responses referring to the memory.

Informativeness Response Generation

A Batch Normalized Inference Network Keeps the KL Vanishing Away

1 code implementation ACL 2020 Qile Zhu, Jianlin Su, Wei Bi, Xiaojiang Liu, Xiyao Ma, Xiaolin Li, Dapeng Wu

Variational Autoencoder (VAE) is widely used as a generative model to approximate a model's posterior on latent variables by combining the amortized variational inference and deep neural networks.

Dialogue Generation Language Modelling +3

The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection

no code implementations EMNLP 2020 Zibo Lin, Deng Cai, Yan Wang, Xiaojiang Liu, Hai-Tao Zheng, Shuming Shi

Despite that response selection is naturally a learning-to-rank problem, most prior works take a point-wise view and train binary classifiers for this task: each response candidate is labeled either relevant (one) or irrelevant (zero).

Conversational Response Selection Learning-To-Rank +2

Prototype-to-Style: Dialogue Generation with Style-Aware Editing on Retrieval Memory

no code implementations5 Apr 2020 Yixuan Su, Yan Wang, Simon Baker, Deng Cai, Xiaojiang Liu, Anna Korhonen, Nigel Collier

A stylistic response generator then takes the prototype and the desired language style as model input to obtain a high-quality and stylistic response.

Dialogue Generation Information Retrieval +1

Stylistic Dialogue Generation via Information-Guided Reinforcement Learning Strategy

no code implementations5 Apr 2020 Yixuan Su, Deng Cai, Yan Wang, Simon Baker, Anna Korhonen, Nigel Collier, Xiaojiang Liu

To enable better balance between the content quality and the style, we introduce a new training strategy, know as Information-Guided Reinforcement Learning (IG-RL).

Dialogue Generation reinforcement-learning +2

Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation

1 code implementation24 Feb 2020 Xiaocheng Feng, Yawei Sun, Bing Qin, Heng Gong, Yibo Sun, Wei Bi, Xiaojiang Liu, Ting Liu

In this paper, we focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer and aims to preserve text styles while altering the content.

Sentence Style Transfer +2

Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering

1 code implementation16 Dec 2019 Jian Wang, Junhao Liu, Wei Bi, Xiaojiang Liu, Kejing He, Ruifeng Xu, Min Yang

In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation.

Dialogue Generation Knowledge Base Question Answering +1

Relevance-Promoting Language Model for Short-Text Conversation

no code implementations26 Nov 2019 Xin Li, Piji Li, Wei Bi, Xiaojiang Liu, Wai Lam

In this paper, we propose to formulate the STC task as a language modeling problem and tailor-make a training strategy to adapt a language model for response generation.

Language Modelling Response Generation +1

A Discrete CVAE for Response Generation on Short-Text Conversation

no code implementations IJCNLP 2019 Jun Gao, Wei Bi, Xiaojiang Liu, Junhui Li, Guodong Zhou, Shuming Shi

In this paper, we introduce a discrete latent variable with an explicit semantic meaning to improve the CVAE on short-text conversation.

Decoder Response Generation +2

Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework

no code implementations IJCNLP 2019 Deng Cai, Yan Wang, Wei Bi, Zhaopeng Tu, Xiaojiang Liu, Shuming Shi

End-to-end sequence generation is a popular technique for developing open domain dialogue systems, though they suffer from the \textit{safe response problem}.

Response Generation Retrieval

Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration

no code implementations IJCNLP 2019 Zhufeng Pan, Kun Bai, Yan Wang, Lianqiang Zhou, Xiaojiang Liu

To facilitate the study of incomplete utterance restoration for open-domain dialogue systems, a large-scale multi-turn dataset Restoration-200K is collected and manually labeled with the explicit relation between an utterance and its context.

Subword ELMo

no code implementations18 Sep 2019 Jiangtong Li, Hai Zhao, Zuchao Li, Wei Bi, Xiaojiang Liu

Embedding from Language Models (ELMo) has shown to be effective for improving many natural language processing (NLP) tasks, and ELMo takes character information to compose word representation to train language models. However, the character is an insufficient and unnatural linguistic unit for word representation. Thus we introduce Embedding from Subword-aware Language Models (ESuLMo) which learns word representation from subwords using unsupervised segmentation over words. We show that ESuLMo can enhance four benchmark NLP tasks more effectively than ELMo, including syntactic dependency parsing, semantic role labeling, implicit discourse relation recognition and textual entailment, which brings a meaningful improvement over ELMo.

Dependency Parsing Natural Language Inference +1

Fine-Grained Sentence Functions for Short-Text Conversation

no code implementations ACL 2019 Wei Bi, Jun Gao, Xiaojiang Liu, Shuming Shi

Classification models are trained on this dataset to (i) recognize the sentence function of new data in a large corpus of short-text conversations; (ii) estimate a proper sentence function of the response given a test query.

Information Retrieval Retrieval +2

Generating Multiple Diverse Responses for Short-Text Conversation

no code implementations14 Nov 2018 Jun Gao, Wei Bi, Xiaojiang Liu, Junhui Li, Shuming Shi

In this paper, we propose a novel response generation model, which considers a set of responses jointly and generates multiple diverse responses simultaneously.

Informativeness Response Generation +1

Translating a Math Word Problem to an Expression Tree

1 code implementation14 Nov 2018 Lei Wang, Yan Wang, Deng Cai, Dongxiang Zhang, Xiaojiang Liu

Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving.

Math Math Word Problem Solving

Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method

1 code implementation EMNLP 2018 Yahui Liu, Wei Bi, Jun Gao, Xiaojiang Liu, Jian Yao, Shuming Shi

We observe that in the conversation tasks, each query could have multiple responses, which forms a 1-to-n or m-to-n relationship in the view of the total corpus.

Dialogue Generation Machine Translation +1

Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory

1 code implementation NAACL 2019 Deng Cai, Yan Wang, Victoria Bi, Zhaopeng Tu, Xiaojiang Liu, Wai Lam, Shuming Shi

Such models rely on insufficient information for generating a specific response since a certain query could be answered in multiple ways.

Dialogue Generation Information Retrieval +3

Language Style Transfer from Sentences with Arbitrary Unknown Styles

no code implementations13 Aug 2018 Yanpeng Zhao, Wei Bi, Deng Cai, Xiaojiang Liu, Kewei Tu, Shuming Shi

Then, by recombining the content with the target style, we decode a sentence aligned in the target domain.

Sentence Sentence ReWriting +1

Generative Stock Question Answering

no code implementations21 Apr 2018 Zhaopeng Tu, Yong Jiang, Xiaojiang Liu, Lei Shu, Shuming Shi

We study the problem of stock related question answering (StockQA): automatically generating answers to stock related questions, just like professional stock analysts providing action recommendations to stocks upon user's requests.

Decoder Question Answering +1

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