Search Results for author: Wenxuan Zhang

Found 24 papers, 17 papers with code

AnswerFact: Fact Checking in Product Question Answering

no code implementations EMNLP 2020 Wenxuan Zhang, Yang Deng, Jing Ma, Wai Lam

Product-related question answering platforms nowadays are widely employed in many E-commerce sites, providing a convenient way for potential customers to address their concerns during online shopping.

Fact Checking Misinformation +1

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

SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation

no code implementations22 Nov 2022 Wenxuan Zhang, Xiaodong Cun, Xuan Wang, Yong Zhang, Xi Shen, Yu Guo, Ying Shan, Fei Wang

We present SadTalker, which generates 3D motion coefficients (head pose, expression) of the 3DMM from audio and implicitly modulates a novel 3D-aware face render for talking head generation.

Talking Head Generation

Towards Generalizable and Robust Text-to-SQL Parsing

1 code implementation23 Oct 2022 Chang Gao, Bowen Li, Wenxuan Zhang, Wai Lam, Binhua Li, Fei Huang, Luo Si, Yongbin Li

Text-to-SQL parsing tackles the problem of mapping natural language questions to executable SQL queries.

SQL Parsing Text-To-Sql

A Simple Baseline that Questions the Use of Pretrained-Models in Continual Learning

1 code implementation10 Oct 2022 Paul Janson, Wenxuan Zhang, Rahaf Aljundi, Mohamed Elhoseiny

With the success of pretraining techniques in representation learning, a number of continual learning methods based on pretrained models have been proposed.

Continual Learning Representation Learning

UniGDD: A Unified Generative Framework for Goal-Oriented Document-Grounded Dialogue

1 code implementation ACL 2022 Chang Gao, Wenxuan Zhang, Wai Lam

The goal-oriented document-grounded dialogue aims at responding to the user query based on the dialogue context and supporting document.

Multi-Task Learning Response Generation

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

1 code implementation2 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

User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems

1 code implementation7 Feb 2022 Yang Deng, Wenxuan Zhang, Wai Lam, Hong Cheng, Helen Meng

In this paper, we propose a novel framework, namely USDA, to incorporate the sequential dynamics of dialogue acts for predicting user satisfaction, by jointly learning User Satisfaction Estimation and Dialogue Act Recognition tasks.

Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling

1 code implementation27 Dec 2021 Yang Deng, Yaliang Li, Wenxuan Zhang, Bolin Ding, Wai Lam

Recently, Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant and improve the customer shopping experience.

Answer Generation Question Answering

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

Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation

no code implementations WNUT (ACL) 2021 Yang Deng, Wenxuan Zhang, Wai Lam

In this work, we propose a novel and easy-to-apply data augmentation strategy, namely Bilateral Generation (BiG), with a contrastive training objective for improving the performance of ranking question answer pairs with existing labeled data.

Answer Generation Data Augmentation +3

Answering Product-related Questions with Heterogeneous Information

no code implementations Asian Chapter of the Association for Computational Linguistics 2020 Wenxuan Zhang, Qian Yu, Wai Lam

Providing instant response for product-related questions in E-commerce question answering platforms can greatly improve users{'} online shopping experience.

Question Answering

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

Multi-hop Inference for Question-driven Summarization

1 code implementation EMNLP 2020 Yang Deng, Wenxuan Zhang, Wai Lam

Question-driven summarization has been recently studied as an effective approach to summarizing the source document to produce concise but informative answers for non-factoid questions.

Abstractive Text Summarization

Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce

1 code implementation27 Aug 2020 Yang Deng, Wenxuan Zhang, Wai Lam

There are two main challenges when exploiting the opinion information from the reviews to facilitate the opinion-aware answer generation: (i) jointly modeling opinionated and interrelated information between the question and reviews to capture important information for answer generation, (ii) aggregating diverse opinion information to uncover the common opinion towards the given question.

Answer Generation Opinion Mining +1

Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling

no code implementations28 Jun 2020 Wenxuan Zhang, Yang Deng, Wai Lam

In this paper, we investigate the answer ranking problem for product-related questions, with the relevant reviews treated as auxiliary information that can be exploited for facilitating the ranking.

Natural Language Inference Question Answering

Review-guided Helpful Answer Identification in E-commerce

1 code implementation13 Mar 2020 Wenxuan Zhang, Wai Lam, Yang Deng, Jing Ma

In this paper, we propose the Review-guided Answer Helpfulness Prediction (RAHP) model that not only considers the interactions between QA pairs but also investigates the opinion coherence between the answer and crowds' opinions reflected in the reviews, which is another important factor to identify helpful answers.

Answer Selection Community Question Answering

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

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