Search Results for author: Meishan Zhang

Found 72 papers, 37 papers with code

A Fine-Grained Domain Adaption Model for Joint Word Segmentation and POS Tagging

1 code implementation EMNLP 2021 Peijie Jiang, Dingkun Long, Yueheng Sun, Meishan Zhang, Guangwei Xu, Pengjun Xie

Self-training is one promising solution for it, which struggles to construct a set of high-quality pseudo training instances for the target domain.

Domain Adaptation POS +2

APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing

no code implementations Findings (EMNLP) 2021 Ying Li, Meishan Zhang, Zhenghua Li, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan

Thanks to the strong representation learning capability of deep learning, especially pre-training techniques with language model loss, dependency parsing has achieved great performance boost in the in-domain scenario with abundant labeled training data for target domains.

Dependency Parsing Language Modelling +1

RST Discourse Parsing with Second-Stage EDU-Level Pre-training

1 code implementation ACL 2022 Nan Yu, Meishan Zhang, Guohong Fu, Min Zhang

Pre-trained language models (PLMs) have shown great potentials in natural language processing (NLP) including rhetorical structure theory (RST) discourse parsing. Current PLMs are obtained by sentence-level pre-training, which is different from the basic processing unit, i. e. element discourse unit (EDU). To this end, we propose a second-stage EDU-level pre-training approach in this work, which presents two novel tasks to learn effective EDU representations continually based on well pre-trained language models. Concretely, the two tasks are (1) next EDU prediction (NEP) and (2) discourse marker prediction (DMP). We take a state-of-the-art transition-based neural parser as baseline, and adopt it with a light bi-gram EDU modification to effectively explore the EDU-level pre-trained EDU representation. Experimental results on a benckmark dataset show that our method is highly effective, leading a 2. 1-point improvement in F1-score. All codes and pre-trained models will be released publicly to facilitate future studies.

Discourse Marker Prediction Discourse Parsing

Cross-Lingual Dependency Parsing via Self-Training

no code implementations CCL 2020 Meishan Zhang, Yue Zhang

Recent advances of multilingual word representations weaken the input divergences across languages, making cross-lingual transfer similar to the monolingual cross-domain and semi-supervised settings.

Cross-Lingual POS Tagging Cross-Lingual Transfer +2

RankingGPT: Empowering Large Language Models in Text Ranking with Progressive Enhancement

no code implementations28 Nov 2023 Longhui Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Meishan Zhang, Min Zhang

Text ranking is a critical task in various information retrieval applications, and the recent success of Large Language Models (LLMs) in natural language processing has sparked interest in their application to text ranking.

Information Retrieval

LLM-enhanced Self-training for Cross-domain Constituency Parsing

1 code implementation5 Nov 2023 Jianling Li, Meishan Zhang, Peiming Guo, Min Zhang, Yue Zhang

Our experimental results demonstrate that self-training for constituency parsing, equipped with an LLM, outperforms traditional methods regardless of the LLM's performance.

Constituency Parsing Language Modelling +1

Language Models are Universal Embedders

1 code implementation12 Oct 2023 Xin Zhang, Zehan Li, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Meishan Zhang, Min Zhang

As such cases span from English to other natural or programming languages, from retrieval to classification and beyond, it is desirable to build a unified embedding model rather than dedicated ones for each scenario.

Code Search Language Modelling +2

Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role Labeling

no code implementations9 Aug 2023 Yu Zhao, Hao Fei, Yixin Cao, Bobo Li, Meishan Zhang, Jianguo Wei, Min Zhang, Tat-Seng Chua

A scene-event mapping mechanism is first designed to bridge the gap between the underlying scene structure and the high-level event semantic structure, resulting in an overall hierarchical scene-event (termed ICE) graph structure.

Semantic Role Labeling

XNLP: An Interactive Demonstration System for Universal Structured NLP

no code implementations3 Aug 2023 Hao Fei, Meishan Zhang, Min Zhang, Tat-Seng Chua

Structured Natural Language Processing (XNLP) is an important subset of NLP that entails understanding the underlying semantic or syntactic structure of texts, which serves as a foundational component for many downstream applications.

Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction

no code implementations20 May 2023 Hao Fei, Meishan Zhang, Min Zhang, Tat-Seng Chua

Latest efforts on cross-lingual relation extraction (XRE) aggressively leverage the language-consistent structural features from the universal dependency (UD) resource, while they may largely suffer from biased transfer (e. g., either target-biased or source-biased) due to the inevitable linguistic disparity between languages.

Relation Extraction

Scene Graph as Pivoting: Inference-time Image-free Unsupervised Multimodal Machine Translation with Visual Scene Hallucination

1 code implementation20 May 2023 Hao Fei, Qian Liu, Meishan Zhang, Min Zhang, Tat-Seng Chua

In this work, we investigate a more realistic unsupervised multimodal machine translation (UMMT) setup, inference-time image-free UMMT, where the model is trained with source-text image pairs, and tested with only source-text inputs.

Multimodal Machine Translation Translation

Generating Visual Spatial Description via Holistic 3D Scene Understanding

1 code implementation19 May 2023 Yu Zhao, Hao Fei, Wei Ji, Jianguo Wei, Meishan Zhang, Min Zhang, Tat-Seng Chua

With an external 3D scene extractor, we obtain the 3D objects and scene features for input images, based on which we construct a target object-centered 3D spatial scene graph (Go3D-S2G), such that we model the spatial semantics of target objects within the holistic 3D scenes.

Scene Understanding Text Generation

On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training

no code implementations19 Apr 2023 Hao Fei, Tat-Seng Chua, Chenliang Li, Donghong Ji, Meishan Zhang, Yafeng Ren

In this study, we propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training.

Aspect-Based Sentiment Analysis (ABSA) Contrastive Learning +1

LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model

1 code implementation13 Apr 2023 Hao Fei, Shengqiong Wu, Jingye Li, Bobo Li, Fei Li, Libo Qin, Meishan Zhang, Min Zhang, Tat-Seng Chua

Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under a GLM.

Language Modelling UIE

Improving Simultaneous Machine Translation with Monolingual Data

1 code implementation2 Dec 2022 Hexuan Deng, Liang Ding, Xuebo Liu, Meishan Zhang, DaCheng Tao, Min Zhang

Preliminary experiments on En-Zh and En-Ja news domain corpora demonstrate that monolingual data can significantly improve translation quality (e. g., +3. 15 BLEU on En-Zh).

Knowledge Distillation Machine Translation +2

Extending Phrase Grounding with Pronouns in Visual Dialogues

1 code implementation23 Oct 2022 Panzhong Lu, Xin Zhang, Meishan Zhang, Min Zhang

First, we construct a dataset of phrase grounding with both noun phrases and pronouns to image regions.

Phrase Grounding

Visual Spatial Description: Controlled Spatial-Oriented Image-to-Text Generation

1 code implementation20 Oct 2022 Yu Zhao, Jianguo Wei, Zhichao Lin, Yueheng Sun, Meishan Zhang, Min Zhang

Accordingly, we manually annotate a dataset to facilitate the investigation of the newly-introduced task and build several benchmark encoder-decoder models by using VL-BART and VL-T5 as backbones.

Image Captioning Text Generation

Domain-Specific NER via Retrieving Correlated Samples

1 code implementation COLING 2022 Xin Zhang, Yong Jiang, Xiaobin Wang, Xuming Hu, Yueheng Sun, Pengjun Xie, Meishan Zhang

Successful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge.

Named Entity Recognition

Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations

1 code implementation ACL 2022 Xin Zhang, Guangwei Xu, Yueheng Sun, Meishan Zhang, Xiaobin Wang, Min Zhang

Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus, which could be extremely difficult to satisfy.

AISHELL-NER: Named Entity Recognition from Chinese Speech

1 code implementation17 Feb 2022 Boli Chen, Guangwei Xu, Xiaobin Wang, Pengjun Xie, Meishan Zhang, Fei Huang

Named Entity Recognition (NER) from speech is among Spoken Language Understanding (SLU) tasks, aiming to extract semantic information from the speech signal.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

Quantifying Robustness to Adversarial Word Substitutions

no code implementations11 Jan 2022 Yuting Yang, Pei Huang, Feifei Ma, Juan Cao, Meishan Zhang, Jian Zhang, Jintao Li

Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations.

Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role Labeling

1 code implementation5 Oct 2021 Shengqiong Wu, Hao Fei, Fei Li, Donghong Ji, Meishan Zhang, Yijiang Liu, Chong Teng

Unified opinion role labeling (ORL) aims to detect all possible opinion structures of 'opinion-holder-target' in one shot, given a text.

 Ranked #1 on Fine-Grained Opinion Analysis on MPQA (F1 (Opinion) metric)

Fine-Grained Opinion Analysis

A Graph-Based Neural Model for End-to-End Frame Semantic Parsing

1 code implementation EMNLP 2021 Zhichao Lin, Yueheng Sun, Meishan Zhang

The three subtasks are closely related while previous studies model them individually, which ignores their intern connections and meanwhile induces error propagation problem.

graph construction Semantic Parsing +1

Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition

1 code implementation ACL 2021 Xin Zhang, Guangwei Xu, Yueheng Sun, Meishan Zhang, Pengjun Xie

Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers.

Domain Adaptation named-entity-recognition +3

End-to-end Semantic Role Labeling with Neural Transition-based Model

1 code implementation2 Jan 2021 Hao Fei, Meishan Zhang, Bobo Li, Donghong Ji

It performs the two subtasks of SRL: predicate identification and argument role labeling, jointly.

Semantic Role Labeling

Cross-lingual Semantic Role Labeling with Model Transfer

no code implementations24 Aug 2020 Hao Fei, Meishan Zhang, Fei Li, Donghong Ji

In this paper, we fill the gap of cross-lingual SRL by proposing an end-to-end SRL model that incorporates a variety of universal features and transfer methods.

Semantic Role Labeling

A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures

no code implementations19 Jun 2020 Meishan Zhang

This article briefly reviews the representative models of constituent parsing and dependency parsing, and also dependency graph parsing with rich semantics.

Dependency Parsing Semantic Parsing

DRTS Parsing with Structure-Aware Encoding and Decoding

no code implementations ACL 2020 Qiankun Fu, Yue Zhang, Jiangming Liu, Meishan Zhang

Discourse representation tree structure (DRTS) parsing is a novel semantic parsing task which has been concerned most recently.

Graph Attention Semantic Parsing

End to End Chinese Lexical Fusion Recognition with Sememe Knowledge

no code implementations COLING 2020 Yijiang Liu, Meishan Zhang, Donghong Ji

In this paper, we present Chinese lexical fusion recognition, a new task which could be regarded as one kind of coreference recognition.

Graph Attention

Syntax-aware Neural Semantic Role Labeling

1 code implementation22 Jul 2019 Qingrong Xia, Zhenghua Li, Min Zhang, Meishan Zhang, Guohong Fu, Rui Wang, Luo Si

Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP.

Semantic Parsing Semantic Role Labeling +1

Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling

1 code implementation NAACL 2019 Meishan Zhang, Peili Liang, Guohong Fu

Opinion role labeling (ORL) is an important task for fine-grained opinion mining, which identifies important opinion arguments such as holder and target for a given opinion trigger.

 Ranked #1 on Fine-Grained Opinion Analysis on MPQA (using extra training data)

Fine-Grained Opinion Analysis Opinion Mining +1

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 Test

Combining Discrete and Neural Features for Sequence Labeling

1 code implementation24 Aug 2017 Jie Yang, Zhiyang Teng, Meishan Zhang, Yue Zhang

Our results on standard benchmarks show that state-of-the-art neural models can give accuracies comparable to the best discrete models in the literature for most tasks and combing discrete and neural features unanimously yield better results.

named-entity-recognition Named Entity Recognition +2

Joint POS Tagging and Dependency Parsing with Transition-based Neural Networks

no code implementations25 Apr 2017 Liner Yang, Meishan Zhang, Yang Liu, Nan Yu, Maosong Sun, Guohong Fu

While part-of-speech (POS) tagging and dependency parsing are observed to be closely related, existing work on joint modeling with manually crafted feature templates suffers from the feature sparsity and incompleteness problems.

Dependency Parsing Part-Of-Speech Tagging +1

Tweet Sarcasm Detection Using Deep Neural Network

1 code implementation COLING 2016 Meishan Zhang, Yue Zhang, Guohong Fu

We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features.

Sarcasm Detection

A Bi-LSTM-RNN Model for Relation Classification Using Low-Cost Sequence Features

no code implementations27 Aug 2016 Fei Li, Meishan Zhang, Guohong Fu, Tao Qian, Donghong Ji

This model divides a sentence or text segment into five parts, namely two target entities and their three contexts.

Dependency Parsing General Classification +1

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