Search Results for author: Guohong Fu

Found 32 papers, 16 papers with code

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 +1

Non-autoregressive Text Editing with Copy-aware Latent Alignments

1 code implementation11 Oct 2023 Yu Zhang, Yue Zhang, Leyang Cui, Guohong Fu

In this work, we propose a novel non-autoregressive text editing method to circumvent the above issues, by modeling the edit process with latent CTC alignments.

Management Sentence +1

OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch

1 code implementation19 Sep 2023 Juntao Li, Zecheng Tang, Yuyang Ding, Pinzheng Wang, Pei Guo, Wangjie You, Dan Qiao, Wenliang Chen, Guohong Fu, Qiaoming Zhu, Guodong Zhou, Min Zhang

This report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design, training objectives of different stages, and other enhancement techniques.

RSpell: Retrieval-augmented Framework for Domain Adaptive Chinese Spelling Check

1 code implementation16 Aug 2023 Siqi Song, Qi Lv, Lei Geng, Ziqiang Cao, Guohong Fu

In this paper, we propose a retrieval-augmented spelling check framework called RSpell, which searches corresponding domain terms and incorporates them into CSC models.

Retrieval

Discourse-Aware Emotion Cause Extraction in Conversations

no code implementations26 Oct 2022 Dexin Kong, Nan Yu, Yun Yuan, Guohong Fu, Chen Gong

In this paper, we investigate the importance of discourse structures in handling utterance interactions and conversationspecific features for ECEC.

Causal Emotion Entailment Discourse Parsing +2

Visual Subtitle Feature Enhanced Video Outline Generation

no code implementations24 Aug 2022 Qi Lv, Ziqiang Cao, Wenrui Xie, Derui Wang, Jingwen Wang, Zhiwei Hu, Tangkun Zhang, Ba Yuan, Yuanhang Li, Min Cao, Wenjie Li, Sujian Li, Guohong Fu

Furthermore, based on the similarity between video outlines and textual outlines, we use a large number of articles with chapter headings to pretrain our model.

Headline Generation Navigate +4

Revising Image-Text Retrieval via Multi-Modal Entailment

no code implementations22 Aug 2022 Xu Yan, Chunhui Ai, Ziqiang Cao, Min Cao, Sujian Li, Wenjie Li, Guohong Fu

While the builders of existing image-text retrieval datasets strive to ensure that the caption matches the linked image, they cannot prevent a caption from fitting other images.

Natural Language Inference Retrieval +2

Sentence Matching with Syntax- and Semantics-Aware BERT

no code implementations COLING 2020 Tao Liu, Xin Wang, Chengguo Lv, Ranran Zhen, Guohong Fu

Sentence matching aims to identify the special relationship between two sentences, and plays a key role in many natural language processing tasks.

Sentence

Unseen Target Stance Detection with Adversarial Domain Generalization

1 code implementation12 Oct 2020 Zhen Wang, Qiansheng Wang, Chengguo Lv, Xue Cao, Guohong Fu

Although stance detection has made great progress in the past few years, it is still facing the problem of unseen targets.

Domain Generalization Stance Detection

Cue-word Driven Neural Response Generation with a Shrinking Vocabulary

1 code implementation10 Oct 2020 Qiansheng Wang, Yuxin Liu, Chengguo Lv, Zhen Wang, Guohong Fu

Open-domain response generation is the task of generating sensible and informative re-sponses to the source sentence.

Response Generation Sentence

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 +2

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

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 +2

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 +3

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