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.
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.
1 code implementation • 28 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.
2 code implementations • 6 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).
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.
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.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Heng Gong, Wei Bi, Xiaocheng Feng, Bing Qin, Xiaojiang Liu, Ting Liu
Neural table-to-text models, which select and order salient data, as well as verbalizing them fluently via surface realization, have achieved promising progress.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Yifan Gao, Piji Li, Wei Bi, Xiaojiang Liu, Michael R. Lyu, Irwin King
Besides a small number of high-resource sentence functions, a large portion of sentence functions is infrequent.
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.
no code implementations • 19 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.
no code implementations • 14 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).
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.
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.
1 code implementation • ACL 2020 • Piji Li, Haisong Zhang, Xiaojiang Liu, Shuming Shi
(3) Although they are restricted to some formats, the sentence integrity must be guaranteed.
no code implementations • ACL 2020 • Haoyu Song, Yan Wang, Wei-Nan Zhang, Xiaojiang Liu, Ting Liu
Maintaining a consistent personality in conversations is quite natural for human beings, but is still a non-trivial task for machines.
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).
Ranked #10 on Conversational Response Selection on E-commerce
no code implementations • 5 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.
no code implementations • 5 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).
1 code implementation • 24 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.
1 code implementation • 16 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.
no code implementations • 26 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.
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.
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.
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}.
no code implementations • 18 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.
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.
no code implementations • 14 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.
1 code implementation • 14 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.
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.
no code implementations • EMNLP 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.
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.
no code implementations • 13 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.
no code implementations • ACL 2018 • Lianhui Qin, Lemao Liu, Victoria Bi, Yan Wang, Xiaojiang Liu, Zhiting Hu, Hai Zhao, Shuming Shi
Comments of online articles provide extended views and improve user engagement.
no code implementations • 21 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.
no code implementations • EMNLP 2017 • Yan Wang, Xiaojiang Liu, Shuming Shi
This paper presents a deep neural solver to automatically solve math word problems.
Ranked #4 on Math Word Problem Solving on ALG514