Search Results for author:
Found 74 papers, 34 papers with code
AnswerFact: Fact Checking in Product Question Answering
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.
Aspect-based Sentiment Analysis in Question Answering Forums
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
Aspect-Based Sentiment Analysis (ABSA)
+2
Chain-of-Exemplar: Enhancing Distractor Generation for Multimodal Educational Question Generation
Multiple-choice questions (MCQs) are important in enhancing concept learning and student engagement for educational purposes.
The Oscars of AI Theater: A Survey on Role-Playing with Language Models
This survey explores the burgeoning field of role-playing with language models, focusing on their development from early persona-based models to advanced character-driven simulations facilitated by Large Language Models (LLMs).
Ask-before-Plan: Proactive Language Agents for Real-World Planning
In this work, we introduce a new task, Proactive Agent Planning, which requires language agents to predict clarification needs based on user-agent conversation and agent-environment interaction, invoke external tools to collect valid information, and generate a plan to fulfill the user's demands.
Hello Again! LLM-powered Personalized Agent for Long-term Dialogue
Crucial to addressing this real-world need are event summary and persona management, which enable reasoning for appropriate long-term dialogue responses.
Towards Comprehensive and Efficient Post Safety Alignment of Large Language Models via Safety Patching
Safety alignment of large language models (LLMs) has been gaining increasing attention.
Selective Annotation via Data Allocation: These Data Should Be Triaged to Experts for Annotation Rather Than the Model
As such, informative or hard data is assigned to the expert for annotation, while easy data is handled by the model.
STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents
However, they still struggle to deliver promising performance on unseen domains, struggling to achieve effective domain transferability.
CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models
To this end, we introduce CLAMBER, a benchmark for evaluating LLMs using a well-organized taxonomy.
Co-Matching: Towards Human-Machine Collaborative Legal Case Matching
However, successful legal case matching requires the tacit knowledge of legal practitioners, which is difficult to verbalize and encode into machines.
Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems
This paper aims to efficiently enable large language models (LLMs) to use external knowledge and goal guidance in conversational recommender system (CRS) tasks.
Can Large Language Models Learn the Physics of Metamaterials? An Empirical Study with ChatGPT
LLMs possess some advantages over humans that may give them benefits for research, including the ability to process enormous amounts of data, find hidden patterns in data, and operate in higher-dimensional spaces.
Towards Human-centered Proactive Conversational Agents
Recent research on proactive conversational agents (PCAs) mainly focuses on improving the system's capabilities in anticipating and planning action sequences to accomplish tasks and achieve goals before users articulate their requests.
Concept -- An Evaluation Protocol on Conversational Recommender Systems with System-centric and User-centric Factors
The conversational recommendation system (CRS) has been criticized regarding its user experience in real-world scenarios, despite recent significant progress achieved in academia.
Strength Lies in Differences! Towards Effective Non-collaborative Dialogues via Tailored Strategy Planning
We investigate non-collaborative dialogue agents, which are expected to engage in strategic conversations with diverse users, for securing a mutual agreement that leans favorably towards the system's objectives.
Consecutive Model Editing with Batch alongside HooK Layers
As the typical retraining paradigm is unacceptably time- and resource-consuming, researchers are turning to model editing in order to seek an effective, consecutive, and batch-supportive way to edit the model behavior directly.
Gotcha! Don't trick me with unanswerable questions! Self-aligning Large Language Models for Responding to Unknown Questions
Despite the remarkable abilities of Large Language Models (LLMs) to answer questions, they often display a considerable level of overconfidence even when the question does not have a definitive answer.
On the Multi-turn Instruction Following for Conversational Web Agents
Web agents powered by Large Language Models (LLMs) have demonstrated remarkable abilities in planning and executing multi-step interactions within complex web-based environments, fulfilling a wide range of web navigation tasks.
UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems
To better plan and incorporate the use of multiple sources in generating personalized response, we firstly decompose it into three sub-tasks: Knowledge Source Selection, Knowledge Retrieval, and Response Generation.
Unifying Text, Tables, and Images for Multimodal Question Answering
Multimodal question answering (MMQA), which aims to derive the answer from multiple knowledge modalities (e. g., text, tables, and images), has received increasing attention due to its board applications.
Towards Goal-oriented Intelligent Tutoring Systems in Online Education
In this work, we investigate a new task, named Goal-oriented Intelligent Tutoring Systems (GITS), which aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment.
WatME: Towards Lossless Watermarking Through Lexical Redundancy
Text watermarking has emerged as a pivotal technique for identifying machine-generated text.
Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents
Proactive dialogues serve as a practical yet challenging dialogue problem in the era of large language models (LLMs), where the dialogue policy planning is the key to improving the proactivity of LLMs.
Attack Prompt Generation for Red Teaming and Defending Large Language Models
Furthermore, we propose a defense framework that fine-tunes victim LLMs through iterative interactions with the attack framework to enhance their safety against red teaming attacks.
DepWiGNN: A Depth-wise Graph Neural Network for Multi-hop Spatial Reasoning in Text
Specifically, we design a novel node memory scheme and aggregate the information over the depth dimension instead of the breadth dimension of the graph, which empowers the ability to collect long dependencies without stacking multiple layers.
Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogue
Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses.
Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators
Large language models (LLMs) outperform information retrieval techniques for downstream knowledge-intensive tasks when being prompted to generate world knowledge.
Social Media Fashion Knowledge Extraction as Captioning
Existing work on fashion knowledge extraction in social media is classification-based and requires to manually determine a set of fashion knowledge categories in advance.
Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals
Recently, the development of large language models (LLMs) has been significantly enhanced the question answering and dialogue generation, and makes them become increasingly popular in current practical scenarios.
Building Emotional Support Chatbots in the Era of LLMs
The integration of emotional support into various conversational scenarios presents profound societal benefits, such as social interactions, mental health counseling, and customer service.
A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers
This survey is expected to facilitate researchers' collaborative efforts to address the challenges of real-world RE systems.
Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration
Conversational systems based on Large Language Models (LLMs), such as ChatGPT, show exceptional proficiency in context understanding and response generation.
Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization
Generating persona consistent dialogue response is important for developing an intelligent conversational agent.
Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs
Large Language Models (LLMs), such as \texttt{ChatGPT}, greatly empower dialogue systems with strong language understanding and generation capabilities.
Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations
Unlike empathetic dialogues, the system in emotional support conversations (ESC) is expected to not only convey empathy for comforting the help-seeker, but also proactively assist in exploring and addressing their problems during the conversation.
A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects
Proactive dialogue systems, related to a wide range of real-world conversational applications, equip the conversational agent with the capability of leading the conversation direction towards achieving pre-defined targets or fulfilling certain goals from the system side.
Product Question Answering in E-Commerce: A Survey
Product question answering (PQA), aiming to automatically provide instant responses to customer's questions in E-Commerce platforms, has drawn increasing attention in recent years.
Deep Active Learning for Scientific Computing in the Wild
Deep learning (DL) is revolutionizing the scientific computing community.
PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Tasks
Specifically, a novel Peer Data Augmentation (PeerDA) approach is proposed which employs span pairs with the PR relation as the augmentation data for training.
PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance
To facilitate conversational question answering (CQA) over hybrid contexts in finance, we present a new dataset, named PACIFIC.
ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction
We study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts.
A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems
In this work, we propose a novel Unified MultI-goal conversational recommeNDer system, namely UniMIND.
Universal Segmentation of 33 Anatomies
Firstly, while it is ideal to learn such a model from a large-scale, fully-annotated dataset, it is practically hard to curate such a dataset.
A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges
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
Aspect-Based Sentiment Analysis (ABSA)
User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems
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 Robust Deep Active Learning for Scientific Computing
Deep learning (DL) is revolutionizing the scientific computing community.
Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling
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.
Inverse deep learning methods and benchmarks for artificial electromagnetic material design
Deep learning (DL) inverse techniques have increased the speed of artificial electromagnetic material (AEM) design and improved the quality of resulting devices.
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions
To this end, we propose a physics-infused deep neural network based on the Blaschke products for phase retrieval.
Aspect Sentiment Quad Prediction as Paraphrase Generation
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.
Ranked #3 on
Aspect-Based Sentiment Analysis (ABSA)
on TASD
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Exploiting Reasoning Chains for Multi-hop Science Question Answering
We propose a novel Chain Guided Retriever-reader ({\tt CGR}) framework to model the reasoning chain for multi-hop Science Question Answering.
Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation
However, existing metrics either neglect the intrinsic cause of the factual inconsistency or rely on auxiliary tasks, leading to an unsatisfied correlation with human judgments or increasing the inconvenience of usage in practice.
Towards Generative Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) has received increasing attention recently.
Ranked #4 on
Aspect Sentiment Triplet Extraction
on ASTE-Data-V2
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation
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.
CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
Spine-related diseases have high morbidity and cause a huge burden of social cost.
Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning
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.
Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge
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.
Intra-/Inter-Interaction Network with Latent Interaction Modeling for Multi-turn Response Selection
Multi-turn response selection has been extensively studied and applied to many real-world applications in recent years.
Research on AI Composition Recognition Based on Music Rules
Starting from the essence of the music, the article constructs a music-rule-identifying algorithm through extracting modes, which will identify the stability of the mode of machine-generated music, to judge whether it is artificial intelligent.
Multi-hop Inference for Question-driven Summarization
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.
Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce
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 Ranking for Product-Related Questions via Multiple Semantic Relations Modeling
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.
Review-guided Helpful Answer Identification in E-commerce
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.
Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering
Community question answering (CQA) gains increasing popularity in both academy and industry recently.
Efforts estimation of doctors annotating medical image
In this paper, we propose a new criterion to evaluate efforts of doctors annotating medical image.
Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering
Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection.
A Knowledge Graph Based Solution for Entity Discovery and Linking in Open-Domain Questions
In Question Entity Discovery and Linking (QEDL) problem, traditional methods are challenged because multiple entities in one short question are difficult to be discovered entirely and the incomplete information in short text makes entity linking hard to implement.
Approach for Semi-automatic Construction of Anti-infective Drug Ontology Based on Entity Linking
Experiments show that our selected features have achieved a precision rate of 86. 77%, a recall rate of 89. 03% and an F1 score of 87. 89%.
MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data
In the light of these challenges, we propose a new truth discovery method, MedTruth, for medical knowledge condition discovery, which incorporates prior source quality information into the source reliability estimation procedure, and also utilizes the knowledge triple information for trustworthy information computation.
Databases
Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge
In this paper, we propose Knowledge-aware Attentive Network (KAN), a transfer learning framework for cross-domain answer selection, which uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domains.
A Multi-channel Network with Image Retrieval for Accurate Brain Tissue Segmentation
Magnetic Resonance Imaging (MRI) is widely used in the pathological and functional studies of the brain, such as epilepsy, tumor diagnosis, etc.
DASN:Data-Aware Skilled Network for Accurate MR Brain Tissue Segmentation
It is on the basis of this, we propose a judgement to distinguish data sets that different models are good at.
A Strategy of MR Brain Tissue Images' Suggestive Annotation Based on Modified U-Net
How to choose appropriate training dataset from limited labeled dataset rather than the whole also has great significance in saving training time.
