Search Results for author: Lidong Bing

Found 79 papers, 38 papers with code

Aspect-based Sentiment Analysis in Question Answering Forums

1 code implementation Findings (EMNLP) 2021 Wenxuan Zhang, Yang Deng, Xin Li, Lidong Bing, Wai Lam

This motivates us to investigate the task of ABSA on QA forums (ABSA-QA), aiming to jointly detect the discussed aspects and their sentiment polarities for a given QA pair.

Aspect-Based Sentiment Analysis Question Answering

IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks

1 code implementation ACL 2022 Liying Cheng, Lidong Bing, Ruidan He, Qian Yu, Yan Zhang, Luo Si

Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidence for the claims, etc.

Claim-Evidence Pair Extraction (CEPE) Claim Extraction with Stance Classification (CESC) +1

Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation

1 code implementation Findings (ACL) 2022 Qingyu Tan, Ruidan He, Lidong Bing, Hwee Tou Ng

Our model consistently outperforms strong baselines and its performance exceeds the previous SOTA by 1. 36 F1 and 1. 46 Ign_F1 score on the DocRED leaderboard.

Knowledge Distillation Relation Extraction

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

no code implementations2 Mar 2022 Wenxuan Zhang, Xin Li, Yang Deng, Lidong Bing, Wai Lam

More specifically, we provide a new taxonomy for ABSA which organizes existing studies from the axes of concerned sentiment elements, with an emphasis on recent advances of compound ABSA tasks.

Aspect-Based Sentiment Analysis

Knowledge Based Multilingual Language Model

no code implementations22 Nov 2021 Linlin Liu, Xin Li, Ruidan He, Lidong Bing, Shafiq Joty, Luo Si

Knowledge enriched language representation learning has shown promising performance across various knowledge-intensive NLP tasks.

Knowledge Graphs Language Modelling +4

MReD: A Meta-Review Dataset for Structure-Controllable Text Generation

1 code implementation Findings (ACL) 2022 Chenhui Shen, Liying Cheng, Ran Zhou, Lidong Bing, Yang You, Luo Si

A more useful text generator should leverage both the input text and the control signal to guide the generation, which can only be built with a deep understanding of the domain knowledge.

Text Generation Text Summarization

Aspect Sentiment Quad Prediction as Paraphrase Generation

1 code implementation EMNLP 2021 Wenxuan Zhang, Yang Deng, Xin Li, Yifei Yuan, Lidong Bing, Wai Lam

Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years, which typically involves four fundamental sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity.

Aspect-Based Sentiment Analysis Paraphrase Generation

Multilingual AMR Parsing with Noisy Knowledge Distillation

1 code implementation Findings (EMNLP) 2021 Deng Cai, Xin Li, Jackie Chun-Sing Ho, Lidong Bing, Wai Lam

We study multilingual AMR parsing from the perspective of knowledge distillation, where the aim is to learn and improve a multilingual AMR parser by using an existing English parser as its teacher.

AMR Parsing Knowledge Distillation

Argument Pair Extraction via Attention-guided Multi-Layer Multi-Cross Encoding

1 code implementation ACL 2021 Liying Cheng, Tianyu Wu, Lidong Bing, Luo Si

Prior research work treats this task as a sequence labeling problem and a binary classification problem on two passages that are directly concatenated together, which has a limitation of not fully utilizing the unique characteristics and inherent relations of two different passages.

Argument Pair Extraction (APE)

Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews

1 code implementation ACL 2021 Junhao Liu, Zhen Hai, Min Yang, Lidong Bing

In addition, we also devise an intra-review coherent reasoning module to identify the coherence between the text content and images of the review, which is a piece of strong evidence for review helpfulness prediction.

MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER

no code implementations ACL 2021 Linlin Liu, Bosheng Ding, Lidong Bing, Shafiq Joty, Luo Si, Chunyan Miao

With the source-language data as well as the translated data, a generation-based multilingual data augmentation method is introduced to further increase diversity by generating synthetic labeled data in multiple languages.

Cross-Lingual NER Data Augmentation +4

Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction

2 code implementations ACL 2021 Lu Xu, Yew Ken Chia, Lidong Bing

Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion term.

Aspect Sentiment Triplet Extraction Term Extraction

On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

no code implementations ACL 2021 Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jia-Wei Low, Lidong Bing, Luo Si

It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating the parameters of adapter modules when learning on a downstream task.

Language Modelling

Better Feature Integration for Named Entity Recognition

1 code implementation NAACL 2021 Lu Xu, Zhanming Jie, Wei Lu, Lidong Bing

We believe this is because both types of features - the contextual information captured by the linear sequences and the structured information captured by the dependency trees may complement each other.

Named Entity Recognition NER

Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings

no code implementations11 Mar 2021 Linlin Liu, Thien Hai Nguyen, Shafiq Joty, Lidong Bing, Luo Si

We operationalize our framework by first proposing a novel sense-aware cross entropy loss to model word senses explicitly.

Cross-Lingual NER Cross-Lingual Word Embeddings +4

Dynamic Topic Tracker for KB-to-Text Generation

no code implementations COLING 2020 Zihao Fu, Lidong Bing, Wai Lam, Shoaib Jameel

Recently, many KB-to-text generation tasks have been proposed to bridge the gap between knowledge bases and natural language by directly converting a group of knowledge base triples into human-readable sentences.

Text Generation

Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model

1 code implementation23 Nov 2020 Juntao Li, Ruidan He, Hai Ye, Hwee Tou Ng, Lidong Bing, Rui Yan

Experimental results show that our proposed method achieves significant performance improvements over the state-of-the-art pretrained cross-lingual language model in the CLCD setting.

Language Modelling Mutual Information Estimation +1

Unsupervised Cross-lingual Adaptation for Sequence Tagging and Beyond

no code implementations23 Oct 2020 Xin Li, Lidong Bing, Wenxuan Zhang, Zheng Li, Wai Lam

Cross-lingual adaptation with multilingual pre-trained language models (mPTLMs) mainly consists of two lines of works: zero-shot approach and translation-based approach, which have been studied extensively on the sequence-level tasks.

Cross-Lingual Transfer Translation

Position-Aware Tagging for Aspect Sentiment Triplet Extraction

4 code implementations EMNLP 2020 Lu Xu, Hao Li, Wei Lu, Lidong Bing

Our observation is that the three elements within a triplet are highly related to each other, and this motivates us to build a joint model to extract such triplets using a sequence tagging approach.

Aspect Sentiment Triplet Extraction

Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans

1 code implementation EMNLP 2020 Lu Xu, Lidong Bing, Wei Lu, Fei Huang

Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features.

Extract Aspect

An Unsupervised Sentence Embedding Method by Mutual Information Maximization

1 code implementation EMNLP 2020 Yan Zhang, Ruidan He, Zuozhu Liu, Kwan Hui Lim, Lidong Bing

However, SBERT is trained on corpus with high-quality labeled sentence pairs, which limits its application to tasks where labeled data is extremely scarce.

Self-Supervised Learning Semantic Textual Similarity +2

Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training

2 code implementations EMNLP 2020 Hai Ye, Qingyu Tan, Ruidan He, Juntao Li, Hwee Tou Ng, Lidong Bing

To improve the robustness of self-training, in this paper we present class-aware feature self-distillation (CFd) to learn discriminative features from PrLMs, in which PrLM features are self-distilled into a feature adaptation module and the features from the same class are more tightly clustered.

Text Classification Unsupervised Domain Adaptation

Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling

no code implementations ACL 2020 Canasai Kruengkrai, Thien Hai Nguyen, Sharifah Mahani Aljunied, Lidong Bing

Exploiting sentence-level labels, which are easy to obtain, is one of the plausible methods to improve low-resource named entity recognition (NER), where token-level labels are costly to annotate.

Classification General Classification +4

Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce

1 code implementation17 May 2020 Juntao Li, Chang Liu, Jian Wang, Lidong Bing, Hongsong Li, Xiaozhong Liu, Dongyan Zhao, Rui Yan

We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language.

Information Retrieval

ENT-DESC: Entity Description Generation by Exploring Knowledge Graph

1 code implementation EMNLP 2020 Liying Cheng, Dekun Wu, Lidong Bing, Yan Zhang, Zhanming Jie, Wei Lu, Luo Si

Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description.

Graph-to-Sequence Knowledge Graphs +1

Salience Estimation with Multi-Attention Learning for Abstractive Text Summarization

no code implementations7 Apr 2020 Piji Li, Lidong Bing, Zhongyu Wei, Wai Lam

Different from neural machine translation, in the task of text summarization, salience estimation for words, phrases or sentences is a critical component, since the output summary is a distillation of the input text.

Abstractive Text Summarization Machine Translation +1

GRET: Global Representation Enhanced Transformer

no code implementations24 Feb 2020 Rongxiang Weng, Hao-Ran Wei, Shu-Jian Huang, Heng Yu, Lidong Bing, Weihua Luo, Jia-Jun Chen

The encoder maps the words in the input sentence into a sequence of hidden states, which are then fed into the decoder to generate the output sentence.

Machine Translation Text Generation +2

Review-based Question Generation with Adaptive Instance Transfer and Augmentation

no code implementations ACL 2020 Qian Yu, Lidong Bing, Qiong Zhang, Wai Lam, Luo Si

We propose an iterative learning framework for handling this challenge via adaptive transfer and augmentation of the training instances with the help of the available user-posed question-answer data.

Question Generation

Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning

no code implementations IJCNLP 2019 Kaisong Song, Lidong Bing, Wei Gao, Jun Lin, Lujun Zhao, Jiancheng Wang, Changlong Sun, Xiaozhong Liu, Qiong Zhang

Customers ask questions and customer service staffs answer their questions, which is the basic service model via multi-turn customer service (CS) dialogues on E-commerce platforms.

Multiple Instance Learning

Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations

no code implementations IJCNLP 2019 Ran Le, Wenpeng Hu, Mingyue Shang, Zhenjun You, Lidong Bing, Dongyan Zhao, Rui Yan

Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario.

Improving Question Generation With to the Point Context

no code implementations IJCNLP 2019 Jingjing Li, Yifan Gao, Lidong Bing, Irwin King, Michael R. Lyu

Question generation (QG) is the task of generating a question from a reference sentence and a specified answer within the sentence.

Question Generation

Exploiting BERT for End-to-End Aspect-based Sentiment Analysis

1 code implementation WS 2019 Xin Li, Lidong Bing, Wenxuan Zhang, Wai Lam

In this paper, we investigate the modeling power of contextualized embeddings from pre-trained language models, e. g. BERT, on the E2E-ABSA task.

Aspect-Based Sentiment Analysis Model Selection

Semi-supervised Text Style Transfer: Cross Projection in Latent Space

no code implementations IJCNLP 2019 Mingyue Shang, Piji Li, Zhenxin Fu, Lidong Bing, Dongyan Zhao, Shuming Shi, Rui Yan

Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data.

Style Transfer Text Style Transfer

Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion

no code implementations IJCNLP 2019 Zihao Wang, Kwun Ping Lai, Piji Li, Lidong Bing, Wai Lam

Therefore, we propose a meta-learning framework that aims at handling infrequent relations with few-shot learning and uncommon entities by using textual descriptions.

Few-Shot Learning Knowledge Graph Completion

Hierarchical Pointer Net Parsing

1 code implementation IJCNLP 2019 Linlin Liu, Xiang Lin, Shafiq Joty, Simeng Han, Lidong Bing

Transition-based top-down parsing with pointer networks has achieved state-of-the-art results in multiple parsing tasks, while having a linear time complexity.

Discourse Parsing

An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction

1 code implementation NAACL 2019 Wang Chen, Hou Pong Chan, Piji Li, Lidong Bing, Irwin King

For further exploiting the power of extraction and retrieval, we propose a neural-based merging module to combine and re-rank the predicted keyphrases from the enhanced generative model, the extractive model, and the retrieved keyphrases.

Keyphrase Generation Multi-Task Learning

Persona-Aware Tips Generation

no code implementations6 Mar 2019 Piji Li, ZiHao Wang, Lidong Bing, Wai Lam

In order to exploit the persona information, we propose a framework based on adversarial variational auto-encoders (aVAE) for persona modeling from the historical tips and reviews of users and items.

Abstractive Text Summarization by Incorporating Reader Comments

no code implementations13 Dec 2018 Shen Gao, Xiuying Chen, Piji Li, Zhaochun Ren, Lidong Bing, Dongyan Zhao, Rui Yan

To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect.

Reader-Aware Summarization

Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates

no code implementations EMNLP 2018 Di Chen, Jiachen Du, Lidong Bing, Ruifeng Xu

Inferring the agreement/disagreement relation in debates, especially in online debates, is one of the fundamental tasks in argumentation mining.

Natural Language Inference Sentiment Analysis

Variational Autoregressive Decoder for Neural Response Generation

no code implementations EMNLP 2018 Jiachen Du, Wenjie Li, Yulan He, Ruifeng Xu, Lidong Bing, Xuan Wang

Combining the virtues of probability graphic models and neural networks, Conditional Variational Auto-encoder (CVAE) has shown promising performance in applications such as response generation.

Response Generation

Generating Distractors for Reading Comprehension Questions from Real Examinations

2 code implementations8 Sep 2018 Yifan Gao, Lidong Bing, Piji Li, Irwin King, Michael R. Lyu

We investigate the task of distractor generation for multiple choice reading comprehension questions from examinations.

Distractor Generation Multiple-choice +1

Difficulty Controllable Generation of Reading Comprehension Questions

no code implementations10 Jul 2018 Yifan Gao, Lidong Bing, Wang Chen, Michael R. Lyu, Irwin King

We investigate the difficulty levels of questions in reading comprehension datasets such as SQuAD, and propose a new question generation setting, named Difficulty-controllable Question Generation (DQG).

Question Generation Reading Comprehension

Learning Domain-Sensitive and Sentiment-Aware Word Embeddings

no code implementations ACL 2018 Bei Shi, Zihao Fu, Lidong Bing, Wai Lam

Given reviews from different domains, some existing methods for word embeddings exploit sentiment information, but they cannot produce domain-sensitive embeddings.

Data Augmentation General Classification +2

Transformation Networks for Target-Oriented Sentiment Classification

1 code implementation ACL 2018 Xin Li, Lidong Bing, Wai Lam, Bei Shi

Between the two layers, we propose a component to generate target-specific representations of words in the sentence, meanwhile incorporate a mechanism for preserving the original contextual information from the RNN layer.

Aspect-Based Sentiment Analysis Classification +1

Aspect Term Extraction with History Attention and Selective Transformation

1 code implementation2 May 2018 Xin Li, Lidong Bing, Piji Li, Wai Lam, Zhimou Yang

Aspect Term Extraction (ATE), a key sub-task in Aspect-Based Sentiment Analysis, aims to extract explicit aspect expressions from online user reviews.

Aspect-Based Sentiment Analysis Term Extraction

Actor-Critic based Training Framework for Abstractive Summarization

no code implementations28 Mar 2018 Piji Li, Lidong Bing, Wai Lam

For the critic, we combine the maximum likelihood estimator with a well designed global summary quality estimator which is a neural network based binary classifier aiming to make the generated summaries indistinguishable from the human-written ones.

Abstractive Text Summarization

Deep Recurrent Generative Decoder for Abstractive Text Summarization

1 code implementation EMNLP 2017 Piji Li, Wai Lam, Lidong Bing, ZiHao Wang

We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN).

Abstractive Text Summarization Variational Inference

Neural Rating Regression with Abstractive Tips Generation for Recommendation

no code implementations1 Aug 2017 Piji Li, ZiHao Wang, Zhaochun Ren, Lidong Bing, Wai Lam

In essence, writing some tips and giving a numerical rating are two facets of a user's product assessment action, expressing the user experience and feelings.

Bootstrapping Distantly Supervised IE using Joint Learning and Small Well-structured Corpora

no code implementations10 Jun 2016 Lidong Bing, Bhuwan Dhingra, Kathryn Mazaitis, Jong Hyuk Park, William W. Cohen

We propose a framework to improve performance of distantly-supervised relation extraction, by jointly learning to solve two related tasks: concept-instance extraction and relation extraction.

Relation Extraction

Distant IE by Bootstrapping Using Lists and Document Structure

no code implementations4 Jan 2016 Lidong Bing, Mingyang Ling, Richard C. Wang, William W. Cohen

Distant labeling for information extraction (IE) suffers from noisy training data.

Abstractive Multi-Document Summarization via Phrase Selection and Merging

no code implementations IJCNLP 2015 Lidong Bing, Piji Li, Yi Liao, Wai Lam, Weiwei Guo, Rebecca J. Passonneau

We propose an abstraction-based multi-document summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases.

Document Summarization Multi-Document Summarization

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