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Found 53 papers, 36 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
Cross-lingual Aspect-based Sentiment Analysis with Aspect Term Code-Switching
Many efforts have been made in solving the Aspect-based sentiment analysis (ABSA) task.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models
We find that even though translation into English can help improve the performance of multilingual NLP tasks for English-centric LLMs, it may not be optimal for all scenarios.
How do Large Language Models Handle Multilingualism?
We introduce a framework that depicts LLMs' processing of multilingual inputs: In the first several layers, LLMs understand the question, converting multilingual inputs into English to facilitate the task-solving phase.
AdaMergeX: Cross-Lingual Transfer with Large Language Models via Adaptive Adapter Merging
In this paper, we acknowledge the mutual reliance between task ability and language ability and direct our attention toward the gap between the target language and the source language on tasks.
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.
Radar-Based Recognition of Static Hand Gestures in American Sign Language
This emphasizes the practicality of our methodology in overcoming data scarcity challenges and advancing the field of automatic gesture recognition in VR and HCI applications.
SeaLLMs -- Large Language Models for Southeast Asia
Despite the remarkable achievements of large language models (LLMs) in various tasks, there remains a linguistic bias that favors high-resource languages, such as English, often at the expense of low-resource and regional languages.
Label Delay in Continual Learning
We introduce a new continual learning framework with explicit modeling of the label delay between data and label streams over time steps.
Bridging the Information Gap Between Domain-Specific Model and General LLM for Personalized Recommendation
Nevertheless, these methods encounter the certain issue that information such as community behavior pattern in RS domain is challenging to express in natural language, which limits the capability of LLMs to surpass state-of-the-art domain-specific models.
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.
SOUL: Towards Sentiment and Opinion Understanding of Language
These findings underscore the challenging nature of the SOUL task for existing models, emphasizing the need for further advancements in sentiment analysis to address its complexities.
Multilingual Jailbreak Challenges in Large Language Models
The experimental results reveal that in the unintentional scenario, the rate of unsafe content increases as the availability of languages decreases.
JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning
Instruction tuning has emerged as a crucial process for harnessing the capabilities of large language models (LLMs) by providing explicit task instructions, leading to improved performance in various tasks.
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.
ARFA: An Asymmetric Receptive Field Autoencoder Model for Spatiotemporal Prediction
Additionally, we construct the RainBench, a large-scale radar echo dataset for precipitation prediction, to address the scarcity of meteorological data in the domain.
Continual Zero-Shot Learning through Semantically Guided Generative Random Walks
The GRW loss augments the training by continually encouraging the model to generate realistic and characterized samples to represent the unseen space.
Overcoming Generic Knowledge Loss with Selective Parameter Update
Our method achieves improvements on the accuracy of the newly learned tasks up to 7% while preserving the pretraining knowledge with a negligible decrease of 0. 9% on a representative control set accuracy.
SLAMB: Accelerated Large Batch Training with Sparse Communication
Distributed training of large deep neural networks requires frequent exchange of massive data between machines, thus communication efficiency is a major concern.
M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models
M3Exam exhibits three unique characteristics: (1) multilingualism, encompassing questions from multiple countries that require strong multilingual proficiency and cultural knowledge; (2) multimodality, accounting for the multimodal nature of many exam questions to test the model's multimodal understanding capability; and (3) multilevel structure, featuring exams from three critical educational periods to comprehensively assess a model's proficiency at different levels.
AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach
In this work, we for the first time propose a challenging argument quadruplet extraction task (AQE), which can provide an all-in-one extraction of four argumentative components, i. e., claims, evidence, evidence types, and stances.
Sentiment Analysis in the Era of Large Language Models: A Reality Check
This paper aims to provide a comprehensive investigation into the capabilities of LLMs in performing various sentiment analysis tasks, from conventional sentiment classification to aspect-based sentiment analysis and multifaceted analysis of subjective texts.
Zero-Shot Text Classification via Self-Supervised Tuning
In this work, we propose a new paradigm based on self-supervised learning to solve zero-shot text classification tasks by tuning the language models with unlabeled data, called self-supervised tuning.
Enhancing Few-shot NER with Prompt Ordering based Data Augmentation
Recently, data augmentation (DA) methods have been proven to be effective for pre-trained language models (PLMs) in low-resource settings, including few-shot named entity recognition (NER).
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.
Easy-to-Hard Learning for Information Extraction
Information extraction (IE) systems aim to automatically extract structured information, such as named entities, relations between entities, and events, from unstructured texts.
Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis
Cross-domain aspect-based sentiment analysis (ABSA) aims to perform various fine-grained sentiment analysis tasks on a target domain by transferring knowledge from a source domain.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions
By keeping acquiring new visual information from BLIP-2's answers, ChatCaptioner is able to generate more enriched image descriptions.
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.
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine Reader
We present Pre-trained Machine Reader (PMR), a novel method for retrofitting pre-trained masked language models (MLMs) to pre-trained machine reading comprehension (MRC) models without acquiring labeled data.
SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation
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.
Towards Generalizable and Robust Text-to-SQL Parsing
Text-to-SQL parsing tackles the problem of mapping natural language questions to executable SQL queries.
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.
A Simple Baseline that Questions the Use of Pretrained-Models in Continual Learning
With the success of pretraining techniques in representation learning, a number of continual learning methods based on pretrained models have been proposed.
UniGDD: A Unified Generative Framework for Goal-Oriented Document-Grounded Dialogue
The goal-oriented document-grounded dialogue aims at responding to the user query based on the dialogue context and supporting document.
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.
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 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.
Domain-Aware Continual Zero-Shot Learning
Accordingly, we propose a Domain-Invariant Network (DIN) to learn factorized features for shifting domains and improved textual representation for unseen classes.
Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems
Math Word Problem (MWP) solving needs to discover the quantitative relationships over natural language narratives.
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
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.
Answering Product-related Questions with Heterogeneous Information
Providing instant response for product-related questions in E-commerce question answering platforms can greatly improve users{'} online shopping experience.
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.
Unsupervised Cross-lingual Adaptation for Sequence Tagging and Beyond
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.
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.
Exploiting BERT for End-to-End Aspect-based Sentiment Analysis
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
Aspect-Based Sentiment Analysis (ABSA)
+1
