Search Results for author: Wai Lam

Found 75 papers, 32 papers with code

Semantic Composition with PSHRG for Derivation Tree Reconstruction from Graph-Based Meaning Representations

no code implementations ACL 2022 Chun Hei Lo, Wai Lam, Hong Cheng

We introduce a data-driven approach to generating derivation trees from meaning representation graphs with probabilistic synchronous hyperedge replacement grammar (PSHRG).

Semantic Composition

On Controlling Fallback Responses for Grounded Dialogue Generation

no code implementations Findings (ACL) 2022 Hongyuan Lu, Wai Lam, Hong Cheng, Helen Meng

We propose a novel framework that automatically generates a control token with the generator to bias the succeeding response towards informativeness for answerable contexts and fallback for unanswerable contexts in an end-to-end manner.

Dialogue Generation Informativeness

Towards Domain-Generalizable Paraphrase Identification by Avoiding the Shortcut Learning

no code implementations RANLP 2021 Xin Shen, Wai Lam

Our method forces the network to learn the necessary features for all the words in the input, which alleviates the shortcut learning problem.

Domain Generalization Paraphrase Identification

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

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

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

no code implementations 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

Parameter-Efficient Tuning by Manipulating Hidden States of Pretrained Language Models For Classification Tasks

no code implementations10 Apr 2022 Haoran Yang, Piji Li, Wai Lam

Continuous prompt tuning which prepends a few trainable vectors to the embeddings of input is one of these methods and has drawn much attention due to its effectiveness and efficiency.

Pretrained Language Models

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

Improving Lexical Embeddings for Robust Question Answering

no code implementations28 Feb 2022 Weiwen Xu, Bowei Zou, Wai Lam, Ai Ti Aw

Recent techniques in Question Answering (QA) have gained remarkable performance improvement with some QA models even surpassed human performance.

Question Answering

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

Partner Personas Generation for Diverse Dialogue Generation

no code implementations27 Nov 2021 Hongyuan Lu, Wai Lam, Hong Cheng, Helen M. Meng

We incorporate reinforcement learning with a dedicatedly designed critic network for reward judgement.

Dialogue Generation Response Generation

Sentiment Analysis of Fashion Related Posts in Social Media

no code implementations15 Nov 2021 Yifei Yuan, Wai Lam

One characteristic of our model is that it extracts fashion attributes and integrates them with the image vision information for effective representation.

Multimodal Sentiment Analysis

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

Exploiting Reasoning Chains for Multi-hop Science Question Answering

1 code implementation Findings (EMNLP) 2021 Weiwen Xu, Yang Deng, Huihui Zhang, Deng Cai, Wai Lam

We propose a novel Chain Guided Retriever-reader ({\tt CGR}) framework to model the reasoning chain for multi-hop Science Question Answering.

Question Answering

Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation

1 code implementation Findings (EMNLP) 2021 Haoran Yang, Wai Lam, Piji Li

Exemplar-Guided Paraphrase Generation (EGPG) aims to generate a target sentence which conforms to the style of the given exemplar while encapsulating the content information of the source sentence.

Contrastive Learning Paraphrase Generation +2

Sentence Structure and Word Relationship Modeling for Emphasis Selection

1 code implementation RANLP 2021 Haoran Yang, Wai Lam

In this paper, we propose a new framework that considers sentence structure via a sentence structure graph and word relationship via a word similarity graph.

Word Similarity

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 +2

Conversational Fashion Image Retrieval via Multiturn Natural Language Feedback

1 code implementation8 Jun 2021 Yifei Yuan, Wai Lam

We study the task of conversational fashion image retrieval via multiturn natural language feedback.

Image Retrieval

Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering

1 code implementation Findings (ACL) 2021 Weiwen Xu, Huihui Zhang, Deng Cai, Wai Lam

Our framework contains three new ideas: (a) {\tt AMR-SG}, an AMR-based Semantic Graph, constructed by candidate fact AMRs to uncover any hop relations among question, answer and multiple facts.

graph construction Knowledge Graphs +2

Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning

no code implementations20 May 2021 Yang Deng, Yaliang Li, Fei Sun, Bolin Ding, Wai Lam

However, existing methods mainly target at solving one or two of these three decision-making problems in CRS with separated conversation and recommendation components, which restrict the scalability and generality of CRS and fall short of preserving a stable training procedure.

Decision Making Recommendation Systems +1

Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge

no code implementations12 Apr 2021 Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Wai Lam, Ying Shen

Answer selection, which is involved in many natural language processing applications such as dialog systems and question answering (QA), is an important yet challenging task in practice, since conventional methods typically suffer from the issues of ignoring diverse real-world background knowledge.

Answer Selection Representation Learning

A Theoretical Analysis of the Repetition Problem in Text Generation

1 code implementation29 Dec 2020 Zihao Fu, Wai Lam, Anthony Man-Cho So, Bei Shi

The experimental results show that our theoretical framework is applicable in general generation models and our proposed rebalanced encoding approach alleviates the repetition problem significantly.

Text Generation

Narrative Incoherence Detection

no code implementations21 Dec 2020 Deng Cai, Yizhe Zhang, Yichen Huang, Wai Lam, Bill Dolan

We propose the task of narrative incoherence detection as a new arena for inter-sentential semantic understanding: Given a multi-sentence narrative, decide whether there exist any semantic discrepancies in the narrative flow.

Sentence Embedding

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

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

Enhancing Dialogue Generation via Multi-Level Contrastive Learning

no code implementations19 Sep 2020 Xin Li, Piji Li, Yan Wang, Xiaojiang Liu, Wai Lam

Most of the existing works for dialogue generation are data-driven models trained directly on corpora crawled from websites.

Contrastive Learning Dialogue Generation

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

CoreGen: Contextualized Code Representation Learning for Commit Message Generation

1 code implementation14 Jul 2020 Lun Yiu Nie, Cuiyun Gao, Zhicong Zhong, Wai Lam, Yang Liu, Zenglin Xu

In this paper, we propose a novel Contextualized code representation learning strategy for commit message Generation (CoreGen).

Representation Learning Text Generation

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

Context-aware Helpfulness Prediction for Online Product Reviews

no code implementations27 Apr 2020 Iyiola E. Olatunji, Xin Li, Wai Lam

In this paper, we propose a neural deep learning model that predicts the helpfulness score of a review.

AMR Parsing via Graph-Sequence Iterative Inference

3 code implementations ACL 2020 Deng Cai, Wai Lam

We propose a new end-to-end model that treats AMR parsing as a series of dual decisions on the input sequence and the incrementally constructed graph.

Ranked #2 on AMR Parsing on LDC2014T12 (F1 Full metric)

AMR Parsing Language Modelling

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

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

Relevance-Promoting Language Model for Short-Text Conversation

no code implementations26 Nov 2019 Xin Li, Piji Li, Wei Bi, Xiaojiang Liu, Wai Lam

In this paper, we propose to formulate the STC task as a language modeling problem and tailor-make a training strategy to adapt a language model for response generation.

Language Modelling Response Generation +1

Graph Transformer for Graph-to-Sequence Learning

1 code implementation18 Nov 2019 Deng Cai, Wai Lam

The dominant graph-to-sequence transduction models employ graph neural networks for graph representation learning, where the structural information is reflected by the receptive field of neurons.

AMR-to-Text Generation Graph Representation Learning +4

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

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

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

Core Semantic First: A Top-down Approach for AMR Parsing

1 code implementation IJCNLP 2019 Deng Cai, Wai Lam

The output graph spans the nodes by the distance to the root, following the intuition of first grasping the main ideas then digging into more details.

AMR Parsing

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.

Responding E-commerce Product Questions via Exploiting QA Collections and Reviews

no code implementations COLING 2018 Qian Yu, Wai Lam, ZiHao Wang

Providing instant responses for product questions in E-commerce sites can significantly improve satisfaction of potential consumers.


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 General Classification

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.

Jointly Learning Word Embeddings and Latent Topics

no code implementations21 Jun 2017 Bei Shi, Wai Lam, Shoaib Jameel, Steven Schockaert, Kwun Ping Lai

Word embedding models such as Skip-gram learn a vector-space representation for each word, based on the local word collocation patterns that are observed in a text corpus.

Learning Word Embeddings Topic Models

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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