Search Results for author: Lemao Liu

Found 73 papers, 22 papers with code

Fine-grained Entity Typing without Knowledge Base

1 code implementation EMNLP 2021 Jing Qian, Yibin Liu, Lemao Liu, Yangming Li, Haiyun Jiang, Haisong Zhang, Shuming Shi

Existing work on Fine-grained Entity Typing (FET) typically trains automatic models on the datasets obtained by using Knowledge Bases (KB) as distant supervision.

Entity Typing named-entity-recognition +2

On the Relationship between Neural Machine Translation and Word Alignment

no code implementations Xintong Li, Lemao Liu, Guanlin Li, Max Meng, Shuming Shi

We find that although NMT models are difficult to capture word alignment for CFT words but these words do not sacrifice translation quality significantly, which provides an explanation why NMT is more successful for translation yet worse for word alignment compared to statistical machine translation.

Machine Translation NMT +2

An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing

no code implementations EMNLP 2021 Yi Chen, Haiyun Jiang, Lemao Liu, Shuming Shi, Chuang Fan, Min Yang, Ruifeng Xu

Auxiliary information from multiple sources has been demonstrated to be effective in zero-shot fine-grained entity typing (ZFET).

Entity Typing

Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing

no code implementations ACL 2022 Yi Chen, Jiayang Cheng, Haiyun Jiang, Lemao Liu, Haisong Zhang, Shuming Shi, Ruifeng Xu

In this paper, we firstly empirically find that existing models struggle to handle hard mentions due to their insufficient contexts, which consequently limits their overall typing performance.

Entity Typing

When Graph Data Meets Multimodal: A New Paradigm for Graph Understanding and Reasoning

no code implementations16 Dec 2023 Qihang Ai, Jianwu Zhou, Haiyun Jiang, Lemao Liu, Shuming Shi

Graph data is ubiquitous in the physical world, and it has always been a challenge to efficiently model graph structures using a unified paradigm for the understanding and reasoning on various graphs.

Optical Character Recognition (OCR)

Context Consistency between Training and Testing in Simultaneous Machine Translation

1 code implementation13 Nov 2023 Meizhi Zhong, Lemao Liu, Kehai Chen, Mingming Yang, Min Zhang

Simultaneous Machine Translation (SiMT) aims to yield a real-time partial translation with a monotonically growing the source-side context.

Machine Translation Translation

Hint-enhanced In-Context Learning wakes Large Language Models up for knowledge-intensive tasks

no code implementations3 Nov 2023 Yifan Wang, Qingyan Guo, Xinzhe Ni, Chufan Shi, Lemao Liu, Haiyun Jiang, Yujiu Yang

In-context learning (ICL) ability has emerged with the increasing scale of large language models (LLMs), enabling them to learn input-label mappings from demonstrations and perform well on downstream tasks.

In-Context Learning Open-Domain Question Answering

DistillCSE: Distilled Contrastive Learning for Sentence Embeddings

1 code implementation20 Oct 2023 Jiahao Xu, Wei Shao, Lihui Chen, Lemao Liu

This paper proposes the DistillCSE framework, which performs contrastive learning under the self-training paradigm with knowledge distillation.

Contrastive Learning Knowledge Distillation +2

Towards General Error Diagnosis via Behavioral Testing in Machine Translation

1 code implementation20 Oct 2023 Junjie Wu, Lemao Liu, Dit-yan Yeung

Behavioral testing offers a crucial means of diagnosing linguistic errors and assessing capabilities of NLP models.

Machine Translation Test +1

IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems

1 code implementation17 Oct 2023 Xu Huang, Zhirui Zhang, Ruize Gao, Yichao Du, Lemao Liu, Gouping Huang, Shuming Shi, Jiajun Chen, ShuJian Huang

We present IMTLab, an open-source end-to-end interactive machine translation (IMT) system platform that enables researchers to quickly build IMT systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems.

Machine Translation Translation

Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

1 code implementation3 Sep 2023 Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, Shuming Shi

While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.

Hallucination World Knowledge

Rethinking Translation Memory Augmented Neural Machine Translation

no code implementations12 Jun 2023 Hongkun Hao, Guoping Huang, Lemao Liu, Zhirui Zhang, Shuming Shi, Rui Wang

The finding demonstrates that TM-augmented NMT is good at the ability of fitting data (i. e., lower bias) but is more sensitive to the fluctuations in the training data (i. e., higher variance), which provides an explanation to a recently reported contradictory phenomenon on the same translation task: TM-augmented NMT substantially advances vanilla NMT under the high-resource scenario whereas it fails under the low-resource scenario.

Machine Translation NMT +2

Sen2Pro: A Probabilistic Perspective to Sentence Embedding from Pre-trained Language Model

no code implementations4 Jun 2023 Lingfeng Shen, Haiyun Jiang, Lemao Liu, Shuming Shi

Sentence embedding is one of the most fundamental tasks in Natural Language Processing and plays an important role in various tasks.

Language Modelling Sentence +2

E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition

1 code implementation29 May 2023 Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, Bingzhe Wu

Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments.

named-entity-recognition Named Entity Recognition +1

Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer

1 code implementation22 May 2023 Ruize Gao, Zhirui Zhang, Yichao Du, Lemao Liu, Rui Wang

Nearest Neighbor Machine Translation ($k$NN-MT) has achieved great success in domain adaptation tasks by integrating pre-trained Neural Machine Translation (NMT) models with domain-specific token-level retrieval.

Domain Adaptation Machine Translation +3

SimCSE++: Improving Contrastive Learning for Sentence Embeddings from Two Perspectives

no code implementations22 May 2023 Jiahao Xu, Wei Shao, Lihui Chen, Lemao Liu

This paper improves contrastive learning for sentence embeddings from two perspectives: handling dropout noise and addressing feature corruption.

Contrastive Learning Sentence +1

Frequency-aware Dimension Selection for Static Word Embedding by Mixed Product Distance

no code implementations13 May 2023 Lingfeng Shen, Haiyun Jiang, Lemao Liu, Ying Chen

Static word embedding is still useful, particularly for context-unavailable tasks, because in the case of no context available, pre-trained language models often perform worse than static word embeddings.

Word Embeddings

A Simple and Plug-and-play Method for Unsupervised Sentence Representation Enhancement

no code implementations13 May 2023 Lingfeng Shen, Haiyun Jiang, Lemao Liu, Shuming Shi

Generating proper embedding of sentences through an unsupervised way is beneficial to semantic matching and retrieval problems in real-world scenarios.

Retrieval Sentence +2

Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory

1 code implementation3 May 2023 Xin Cheng, Di Luo, Xiuying Chen, Lemao Liu, Dongyan Zhao, Rui Yan

In this paper, by exploring the duality of the primal problem: better generation also prompts better memory, we propose a novel framework, selfmem, which addresses this limitation by iteratively employing a retrieval-augmented generator to create an unbounded memory pool and using a memory selector to choose one output as memory for the subsequent generation round.

Abstractive Text Summarization Dialogue Generation +2

Federated Nearest Neighbor Machine Translation

no code implementations23 Feb 2023 Yichao Du, Zhirui Zhang, Bingzhe Wu, Lemao Liu, Tong Xu, Enhong Chen

To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention.

Federated Learning Machine Translation +4

Neural Machine Translation with Contrastive Translation Memories

1 code implementation6 Dec 2022 Xin Cheng, Shen Gao, Lemao Liu, Dongyan Zhao, Rui Yan

Retrieval-augmented Neural Machine Translation models have been successful in many translation scenarios.

Contrastive Learning Machine Translation +4

Towards Efficient Dialogue Pre-training with Transferable and Interpretable Latent Structure

no code implementations22 Oct 2022 Xueliang Zhao, Lemao Liu, Tingchen Fu, Shuming Shi, Dongyan Zhao, Rui Yan

With the availability of massive general-domain dialogue data, pre-trained dialogue generation appears to be super appealing to transfer knowledge from the general domain to downstream applications.

Dialogue Generation

Discourse-Aware Graph Networks for Textual Logical Reasoning

no code implementations4 Jul 2022 Yinya Huang, Lemao Liu, Kun Xu, Meng Fang, Liang Lin, Xiaodan Liang

In this work, we propose logic structural-constraint modeling to solve the logical reasoning QA and introduce discourse-aware graph networks (DAGNs).

graph construction Logical Reasoning +3

Efficient Sub-structured Knowledge Distillation

1 code implementation9 Mar 2022 Wenye Lin, Yangming Li, Lemao Liu, Shuming Shi, Hai-Tao Zheng

Specifically, we transfer the knowledge from a teacher model to its student model by locally matching their predictions on all sub-structures, instead of the whole output space.

Knowledge Distillation Structured Prediction

On the Evaluation Metrics for Paraphrase Generation

1 code implementation17 Feb 2022 Lingfeng Shen, Lemao Liu, Haiyun Jiang, Shuming Shi

In this paper we revisit automatic metrics for paraphrase evaluation and obtain two findings that disobey conventional wisdom: (1) Reference-free metrics achieve better performance than their reference-based counterparts.

Machine Translation Paraphrase Generation

A Survey on Retrieval-Augmented Text Generation

no code implementations2 Feb 2022 Huayang Li, Yixuan Su, Deng Cai, Yan Wang, Lemao Liu

Recently, retrieval-augmented text generation attracted increasing attention of the computational linguistics community.

Machine Translation Response Generation +3

Rethink the Evaluation for Attack Strength of Backdoor Attacks in Natural Language Processing

no code implementations9 Jan 2022 Lingfeng Shen, Haiyun Jiang, Lemao Liu, Shuming Shi

It has been shown that natural language processing (NLP) models are vulnerable to a kind of security threat called the Backdoor Attack, which utilizes a `backdoor trigger' paradigm to mislead the models.

Backdoor Attack Text Classification

Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models

1 code implementation12 Dec 2021 Yu Feng, Jing Zhang, Xiaokang Zhang, Lemao Liu, Cuiping Li, Hong Chen

Embedding-based methods are popular for Knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constrained questions.

Data Augmentation Knowledge Base Question Answering

GWLAN: General Word-Level AutocompletioN for Computer-Aided Translation

no code implementations ACL 2021 Huayang Li, Lemao Liu, Guoping Huang, Shuming Shi

In this paper, we propose the task of general word-level autocompletion (GWLAN) from a real-world CAT scenario, and construct the first public benchmark to facilitate research in this topic.

Sentence Translation

Assessing Dialogue Systems with Distribution Distances

1 code implementation Findings (ACL) 2021 Jiannan Xiang, Yahui Liu, Deng Cai, Huayang Li, Defu Lian, Lemao Liu

An important aspect of developing dialogue systems is how to evaluate and compare the performance of different systems.

Dialogue Evaluation

Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition

1 code implementation ICLR 2021 Yangming Li, Lemao Liu, Shuming Shi

Experiments on synthetic datasets and real-world datasets show that our model is robust to unlabeled entity problem and surpasses prior baselines.

named-entity-recognition Named Entity Recognition +2

On the Branching Bias of Syntax Extracted from Pre-trained Language Models

no code implementations Findings of the Association for Computational Linguistics 2020 Huayang Li, Lemao Liu, Guoping Huang, Shuming Shi

Many efforts have been devoted to extracting constituency trees from pre-trained language models, often proceeding in two stages: feature definition and parsing.

Evaluating Explanation Methods for Neural Machine Translation

no code implementations ACL 2020 Jierui Li, Lemao Liu, Huayang Li, Guanlin Li, Guoping Huang, Shuming Shi

Recently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods.

Machine Translation NMT +2

Understanding Learning Dynamics for Neural Machine Translation

no code implementations5 Apr 2020 Conghui Zhu, Guanlin Li, Lemao Liu, Tiejun Zhao, Shuming Shi

Despite the great success of NMT, there still remains a severe challenge: it is hard to interpret the internal dynamics during its training process.

Machine Translation NMT +1

Regularized Context Gates on Transformer for Machine Translation

no code implementations ACL 2020 Xintong Li, Lemao Liu, Rui Wang, Guoping Huang, Max Meng

This paper first provides a method to identify source and target contexts and then introduce a gate mechanism to control the source and target contributions in Transformer.

Machine Translation NMT +1

Neural Machine Translation with Noisy Lexical Constraints

no code implementations13 Aug 2019 Huayang Li, Guoping Huang, Deng Cai, Lemao Liu

Experiments show that our approach can indeed improve the translation quality with the automatically generated constraints.

Machine Translation Open-Ended Question Answering +1

On the Word Alignment from Neural Machine Translation

no code implementations ACL 2019 Xintong Li, Guanlin Li, Lemao Liu, Max Meng, Shuming Shi

Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, this paper finds attention may almost fail to capture word alignment for some NMT models.

Machine Translation NMT +2

Target Foresight Based Attention for Neural Machine Translation

no code implementations NAACL 2018 Xintong Li, Lemao Liu, Zhaopeng Tu, Shuming Shi, Max Meng

In neural machine translation, an attention model is used to identify the aligned source words for a target word (target foresight word) in order to select translation context, but it does not make use of any information of this target foresight word at all.

Language Modelling Machine Translation +1

Neural Machine Translation with Supervised Attention

no code implementations COLING 2016 Lemao Liu, Masao Utiyama, Andrew Finch, Eiichiro Sumita

The attention mechanisim is appealing for neural machine translation, since it is able to dynam- ically encode a source sentence by generating a alignment between a target word and source words.

Machine Translation NMT +2

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