Search Results for author:
Found 118 papers, 60 papers with code
Reader-Aware Multi-Document Summarization via Sparse Coding
We propose a new MDS paradigm called reader-aware multi-document summarization (RA-MDS).
Abstractive Multi-Document Summarization via Phrase Selection and Merging
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.
Jointly Learning Word Embeddings and Latent Topics
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.
Neural Rating Regression with Abstractive Tips Generation for Recommendation
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.
Deep Recurrent Generative Decoder for Abstractive Text Summarization
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).
Ranked #5 on
Text Summarization
on DUC 2004 Task 1
Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset
We investigate the problem of reader-aware multi-document summarization (RA-MDS) and introduce a new dataset for this problem.
Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization
The attention weights are learned automatically by an unsupervised data reconstruction framework which can capture the sentence salience.
Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction
We propose a novel LSTM-based deep multi-task learning framework for aspect term extraction from user review sentences.
Aspect-Based Sentiment Analysis (ABSA)
Multi-Task Learning
+2
Actor-Critic based Training Framework for Abstractive Summarization
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.
QuaSE: Accurate Text Style Transfer under Quantifiable Guidance
For example, an input sequence could be a word sequence, such as review sentence and advertisement text.
Aspect Term Extraction with History Attention and Selective Transformation
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
Aspect-Based Sentiment Analysis (ABSA)
+2
Transformation Networks for Target-Oriented Sentiment Classification
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.
Ranked #19 on
Aspect-Based Sentiment Analysis (ABSA)
on SemEval-2014 Task-4
(Laptop (Acc) metric)
Learning Domain-Sensitive and Sentiment-Aware Word Embeddings
Given reviews from different domains, some existing methods for word embeddings exploit sentiment information, but they cannot produce domain-sensitive embeddings.
Responding E-commerce Product Questions via Exploiting QA Collections and Reviews
Providing instant responses for product questions in E-commerce sites can significantly improve satisfaction of potential consumers.
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory
Such models rely on insufficient information for generating a specific response since a certain query could be answered in multiple ways.
QuaSE: Sequence Editing under Quantifiable Guidance
For example, an input sequence could be a word sequence, such as review sentence and advertisement text.
A Unified Model for Opinion Target Extraction and Target Sentiment Prediction
Target-based sentiment analysis involves opinion target extraction and target sentiment classification.
Aspect-Based Sentiment Analysis (ABSA)
Sentiment Classification
Persona-Aware Tips Generation
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.
Fact Discovery from Knowledge Base via Facet Decomposition
We also propose a novel auto-encoder based facet component to estimate some facets of the fact.
Core Semantic First: A Top-down Approach for AMR Parsing
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.
Ranked #25 on
AMR Parsing
on LDC2017T10
Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion
Therefore, we propose a meta-learning framework that aims at handling infrequent relations with few-shot learning and uncommon entities by using textual descriptions.
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
Review-based Question Generation with Adaptive Instance Transfer and Augmentation
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.
Graph Transformer for Graph-to-Sequence Learning
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.
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.
Relevance-Promoting Language Model for Short-Text Conversation
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.
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.
Salience Estimation with Multi-Attention Learning for Abstractive Text Summarization
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.
AMR Parsing via Graph-Sequence Iterative Inference
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 #3 on
AMR Parsing
on LDC2014T12
Context-aware Helpfulness Prediction for Online Product Reviews
In this paper, we propose a neural deep learning model that predicts the helpfulness score of a review.
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.
CoreGen: Contextualized Code Representation Learning for Commit Message Generation
In this paper, we propose a novel Contextualized code representation learning strategy for commit message Generation (CoreGen).
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.
Enhancing Dialogue Generation via Multi-Level Contrastive Learning
Most of the existing works for dialogue generation are data-driven models trained directly on corpora crawled from websites.
Partially-Aligned Data-to-Text Generation with Distant Supervision
This kind of data is much easier to obtain since it can be produced automatically.
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.
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.
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.
Dynamic Topic Tracker for KB-to-Text Generation
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.
Point-of-Interest Oriented Question Answering with Joint Inference of Semantic Matching and Distance Correlation
Point-of-Interest (POI) oriented question answering (QA) aims to return a list of POIs given a question issued by a user.
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.
Unsupervised KB-to-Text Generation with Auxiliary Triple Extraction using Dual Learning
In our architecture, we reconstruct KB triples or texts via a closed-loop framework via linking a generator and an extractor.
Narrative Incoherence Detection
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.
A Theoretical Analysis of the Repetition Problem in Text Generation
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.
Unstructured Knowledge Access in Task-oriented Dialog Modeling using Language Inference, Knowledge Retrieval and Knowledge-Integrative Response Generation
Dialog systems enriched with external knowledge can handle user queries that are outside the scope of the supporting databases/APIs.
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.
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.
Neural Machine Translation with Monolingual Translation Memory
Second, the memory retriever and NMT model can be jointly optimized for the ultimate translation goal.
Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering
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.
Conversational Fashion Image Retrieval via Multiturn Natural Language Feedback
We study the task of conversational fashion image retrieval via multiturn natural language feedback.
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.
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
Sentence Structure and Word Relationship Modeling for Emphasis Selection
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.
Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation
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.
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.
Multilingual AMR Parsing with Noisy Knowledge Distillation
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.
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
Sentiment Analysis of Fashion Related Posts in Social Media
One characteristic of our model is that it extracts fashion attributes and integrates them with the image vision information for effective representation.
Partner Personas Generation for Diverse Dialogue Generation
We incorporate reinforcement learning with a dedicatedly designed critic network for reward judgement.
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.
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.
Improving Lexical Embeddings for Robust Question Answering
Recent techniques in Question Answering (QA) have gained remarkable performance improvement with some QA models even surpassed human performance.
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)
Parameter-Efficient Tuning by Manipulating Hidden States of Pretrained Language Models For Classification Tasks
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.
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.
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.
PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation
This paper presents \textbf{PCC}: \textbf{P}araphrasing with Bottom-k Sampling and \textbf{C}yclic Learning for \textbf{C}urriculum Data Augmentation, a novel CDA framework via paraphrasing, which exploits the textual paraphrase similarity as the curriculum difficulty measure.
Informative Text Generation from Knowledge Triples
As the development of the encoder-decoder architecture, researchers are able to study the text generation tasks with broader types of data.
Revamping Multilingual Agreement Bidirectionally via Switched Back-translation for Multilingual Neural Machine Translation
Despite the fact that multilingual agreement (MA) has shown its importance for multilingual neural machine translation (MNMT), current methodologies in the field have two shortages: (i) require parallel data between multiple language pairs, which is not always realistic and (ii) optimize the agreement in an ambiguous direction, which hampers the translation performance.
ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction
We study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts.
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.
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.
Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation
Unlike most prior work that only evaluates the ability to measure semantic similarity, we present a thorough evaluation of existing multilingual sentence embeddings and our improved versions, which include a collection of five transfer tasks in different downstream applications.
McQueen: a Benchmark for Multimodal Conversational Query Rewrite
In this paper, we propose the task of multimodal conversational query rewrite (McQR), which performs query rewrite under the multimodal visual conversation setting.
Towards Generalizable and Robust Text-to-SQL Parsing
Text-to-SQL parsing tackles the problem of mapping natural language questions to executable SQL queries.
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.
Advancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models
Despite the success of multilingual sequence-to-sequence pre-training, most existing approaches rely on document-level monolingual corpora in many different languages, sentence-level bilingual corpora,\footnote{In this paper, we use `bilingual corpora' to denote parallel corpora with `bilingual translation pairs' in many different language pairs, each consisting of two sentences/documents with the same meaning written in different languages.
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+4
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.
Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder
Grounded on our analysis, we propose a novel partial attention language model to solve the attention degeneration problem.
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.
Chain-of-Dictionary Prompting Elicits Translation in Large Language Models
Large language models (LLMs) have shown surprisingly good performance in multilingual neural machine translation (MNMT) even when trained without parallel data.
Progressive Translation: Improving Domain Robustness of Neural Machine Translation with Intermediate Sequences
Furthermore, we introduce a full-permutation multi-task learning to alleviate the spurious causal relations from intermediate sequences to the target, which results from exposure bias.
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.
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.
Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models
To this end, we propose a novel method called CoS (Chain-of-Symbol Prompting) that represents the complex environments with condensed symbolic spatial representations during the chained intermediate thinking steps.
A Frustratingly Simple Decoding Method for Neural Text Generation
We introduce a frustratingly simple, super efficient and surprisingly effective decoding method, which we call Frustratingly Simple Decoding (FSD), for neural text generation.
mPMR: A Multilingual Pre-trained Machine Reader at Scale
mPMR aims to guide multilingual pre-trained language models (mPLMs) to perform natural language understanding (NLU) including both sequence classification and span extraction in multiple languages.
Enhancing Grammatical Error Correction Systems with Explanations
Grammatical error correction systems improve written communication by detecting and correcting language mistakes.
A Comprehensive Survey on Deep Learning for Relation Extraction: Recent Advances and New Frontiers
This survey is expected to facilitate researchers' collaborative efforts to tackle the challenges of real-life RE systems.
EPA: Easy Prompt Augmentation on Large Language Models via Multiple Sources and Multiple Targets
This is well motivated as augmenting data via paraphrasing effectively improves neural language models.
Distributional Inclusion Hypothesis and Quantifications: Probing for Hypernymy in Functional Distributional Semantics
Functional Distributional Semantics (FDS) models the meaning of words by truth-conditional functions.
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.
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.
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.
Once Upon a $\textit{Time}$ in $\textit{Graph}$: Relative-Time Pretraining for Complex Temporal Reasoning
However, the knowledge-time association is usually insufficient for the downstream tasks that require reasoning over temporal dependencies between knowledge.
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.
StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving
Most existing chain-of-thought (CoT) prompting methods suffer from the issues of generalizability and consistency, as they often rely on instance-specific solutions that may not be applicable to other cases and lack task-level consistency in their reasoning steps.
Neuro-Symbolic Integration Brings Causal and Reliable Reasoning Proofs
Though prompting LLMs with various reasoning structures produces reasoning proofs along with answers, these proofs are not ensured to be causal and reliable due to the inherent defects of LLMs.
Reasons to Reject? Aligning Language Models with Judgments
As humans, we consistently engage in interactions with our peers and receive feedback in the form of natural language.
A Thorough Examination of Decoding Methods in the Era of LLMs
Decoding methods play an indispensable role in converting language models from next-token predictors into practical task solvers.
Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Search
Therefore, we propose to add images to clarifying questions and formulate the novel task of asking multimodal clarifying questions in open-domain, mixed-initiative conversational search systems.
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs).
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.
Exploring Safety Generalization Challenges of Large Language Models via Code
The rapid advancement of Large Language Models (LLMs) has brought about remarkable generative capabilities but also raised concerns about their potential misuse.
Unveiling the Generalization Power of Fine-Tuned Large Language Models
While Large Language Models (LLMs) have demonstrated exceptional multitasking abilities, fine-tuning these models on downstream, domain-specific datasets is often necessary to yield superior performance on test sets compared to their counterparts without fine-tuning.
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite
To this end, we study low-resource generative conversational query rewrite that is robust to both noise and language style shift.
VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?
Multimodal Large Language models (MLLMs) have shown promise in web-related tasks, but evaluating their performance in the web domain remains a challenge due to the lack of comprehensive benchmarks.
Semantic Composition with PSHRG for Derivation Tree Reconstruction from Graph-Based Meaning Representations
We introduce a data-driven approach to generating derivation trees from meaning representation graphs with probabilistic synchronous hyperedge replacement grammar (PSHRG).
Partner Personas Generation for Dialogue Response Generation
Incorporating personas information allows diverse and engaging responses in dialogue response generation.
Towards Domain-Generalizable Paraphrase Identification by Avoiding the Shortcut Learning
Our method forces the network to learn the necessary features for all the words in the input, which alleviates the shortcut learning problem.
On Controlling Fallback Responses for Grounded Dialogue Generation
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.
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
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
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.
