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Found 121 papers, 76 papers with code
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 (ABSA)
Cross-Lingual Transfer
APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning
Peer review and rebuttal, with rich interactions and argumentative discussions in between, are naturally a good resource to mine arguments.
Ranked #3 on
Argument Pair Extraction (APE)
on RR
Domain Generalization for Text Classification with Memory-Based Supervised Contrastive Learning
While there is much research on cross-domain text classification, most existing approaches focus on one-to-one or many-to-one domain adaptation.
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.
Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts
Large language models (LLMs) are known to effectively perform tasks by simply observing few exemplars.
Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data
We conducted experiments on document-level and biomedical relation extraction datasets, and the results showed that our proposed self-training framework consistently outperforms existing competitive methods on the Re-DocRED and ChemDisgene datasets when the training data are incompletely annotated.
Towards Benchmarking and Improving the Temporal Reasoning Capability of Large Language Models
In this paper, we introduce a comprehensive probing dataset \tempreason to evaluate the temporal reasoning capability of large language models.
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.
INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models
Instruction-tuned large language models have revolutionized natural language processing and have shown great potential in applications such as conversational agents.
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
For the second challenge, we leverage ImageBind, a universal embedding model aligning multiple modalities as the pre-trained audio encoder, and introduce an Audio Q-former on top of ImageBind to learn reasonable auditory query embeddings for the LLM module.
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.
Unlocking Temporal Question Answering for Large Language Models Using Code Execution
Our preliminary experiments show that generating intermediate reasoning steps does not always boost the performance of complex temporal question-answering tasks.
Is GPT-4 a Good Data Analyst?
As large language models (LLMs) have demonstrated their powerful capabilities in plenty of domains and tasks, including context understanding, code generation, language generation, data storytelling, etc., many data analysts may raise concerns if their jobs will be replaced by AI.
Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) is a subtask of Aspect-Based Sentiment Analysis (ABSA) that considers each opinion term, their expressed sentiment, and the corresponding aspect targets.
Aspect-Based Sentiment Analysis (ABSA)
Aspect Sentiment Triplet Extraction
+1
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.
Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning
In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the linguistic gap by training on pseudo-labeled target-language data.
Better Sampling of Negatives for Distantly Supervised Named Entity Recognition
Distantly supervised named entity recognition (DS-NER) has been proposed to exploit the automatically labeled training data instead of human annotations.
Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction
Multimodal Review Helpfulness Prediction (MRHP) aims to rank product reviews based on predicted helpfulness scores and has been widely applied in e-commerce via presenting customers with useful reviews.
Are Large Language Models Good Evaluators for Abstractive Summarization?
We also bring attention to the LLM's deteriorating evaluation capability with the rising qualities of summaries.
Chain of Knowledge: A Framework for Grounding Large Language Models with Structured Knowledge Bases
We introduce Chain of Knowledge (CoK), a framework that augments large language models with structured knowledge bases to improve factual correctness and reduce hallucination.
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).
Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling
First, we represent the fine-grained semantic structures of the input image and text with the visual and textual scene graphs, which are further fused into a unified cross-modal graph (CMG).
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.
Reasoning Implicit Sentiment with Chain-of-Thought Prompting
While sentiment analysis systems try to determine the sentiment polarities of given targets based on the key opinion expressions in input texts, in implicit sentiment analysis (ISA) the opinion cues come in an implicit and obscure manner.
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.
A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization
Pre-trained language models (PLMs) have accomplished impressive achievements in abstractive single-document summarization (SDS).
Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework
As large language models (LLMs) have become the norm in NLP, demonstrating good performance in generation and reasoning tasks, one of its most fatal disadvantages is the lack of factual correctness.
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
To enable further research on PEFT methods of LLMs, this paper presents LLM-Adapters, an easy-to-use framework that integrates various adapters into LLMs and can execute these adapter-based PEFT methods of LLMs for different tasks.
Towards Integration of Discriminability and Robustness for Document-Level Relation Extraction
In addition, we introduce two new data regimes to mimic more realistic scenarios with annotation errors and evaluate our sampling strategy.
Is GPT-3 a Good Data Annotator?
In this paper, we evaluate the performance of GPT-3 as a data annotator by comparing it with traditional data annotation methods and analyzing its output on a range of tasks.
Does GPT-3 Demonstrate Psychopathy? Evaluating Large Language Models from a Psychological Perspective
On the basis of the findings, we recommended the application of more systematic and comprehensive psychological metrics to further evaluate and improve the safety of LLMs.
From Clozing to Comprehending: 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.
A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach
Hence, we propose CubeRE, a cube-filling model inspired by table-filling approaches and explicitly considers the interaction between relation triplets and qualifiers.
Ranked #1 on
Hyper-Relational Extraction
on HyperRED
ConNER: Consistency Training for Cross-lingual Named Entity Recognition
We propose ConNER as a novel consistency training framework for cross-lingual NER, which comprises of: (1) translation-based consistency training on unlabeled target-language data, and (2) dropoutbased consistency training on labeled source-language data.
Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations
Due to the huge amount of parameters, fine-tuning of pretrained language models (PLMs) is prone to overfitting in the low resource scenarios.
Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions
To overcome the aforementioned issues, we propose Multimodal Contrastive Learning for Multimodal Review Helpfulness Prediction (MRHP) problem, concentrating on mutual information between input modalities to explicitly elaborate cross-modal relations.
SentBS: Sentence-level Beam Search for Controllable Summarization
A wide range of control perspectives have been explored in controllable text generation.
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.
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.
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.
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning
With the boom of e-commerce, Multimodal Review Helpfulness Prediction (MRHP), which aims to sort product reviews according to the predicted helpfulness scores has become a research hotspot.
Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction
We analyze the causes and effects of the overwhelming false negative problem in the DocRED dataset.
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks
Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidence for the claims, etc.
Claim-Evidence Pair Extraction (CEPE)
Claim Extraction with Stance Classification (CESC)
+1
Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation
Our model consistently outperforms strong baselines and its performance exceeds the previous SOTA by 1. 36 F1 and 1. 46 Ign_F1 score on the DocRED leaderboard.
Ranked #2 on
Relation Extraction
on DocRED
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction
We introduce the task setting of Zero-Shot Relation Triplet Extraction (ZeroRTE) to encourage further research in low-resource relation extraction methods.
Ranked #1 on
Zero-shot Relation Triplet Extraction
on Wiki-ZSL
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.
Enhancing Cross-lingual Prompting with Dual Prompt Augmentation
Prompting shows promising results in few-shot scenarios.
Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples
In this work, we explore methods to make better use of the multilingual annotation and language agnostic property of KG triples, and present novel knowledge based multilingual language models (KMLMs) trained directly on the knowledge triples.
MReD: A Meta-Review Dataset for Structure-Controllable Text Generation
A more useful text generator should leverage both the input text and the control signal to guide the generation, which can only be built with a deep understanding of the domain knowledge.
GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems
Much recent progress in task-oriented dialogue (ToD) systems has been driven by available annotation data across multiple domains for training.
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 (ABSA)
Paraphrase Generation
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.
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER
Data augmentation is an effective solution to data scarcity in low-resource scenarios.
Bootstrapped Unsupervised Sentence Representation Learning
As high-quality labeled data is scarce, unsupervised sentence representation learning has attracted much attention.
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 (ABSA)
Aspect Sentiment Triplet Extraction
+1
MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER
With the source-language data as well as the translated data, a generation-based multilingual data augmentation method is introduced to further increase diversity by generating synthetic labeled data in multiple languages.
Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews
In addition, we also devise an intra-review coherent reasoning module to identify the coherence between the text content and images of the review, which is a piece of strong evidence for review helpfulness prediction.
Argument Pair Extraction via Attention-guided Multi-Layer Multi-Cross Encoding
Prior research work treats this task as a sequence labeling problem and a binary classification problem on two passages that are directly concatenated together, which has a limitation of not fully utilizing the unique characteristics and inherent relations of two different passages.
Ranked #2 on
Argument Pair Extraction (APE)
on RR
Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion term.
Ranked #5 on
Aspect Sentiment Triplet Extraction
on ASTE-Data-V2
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation
It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating the parameters of adapter modules when learning on a downstream task.
Better Feature Integration for Named Entity Recognition
We believe this is because both types of features - the contextual information captured by the linear sequences and the structured information captured by the dependency trees may complement each other.
Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings
We operationalize our framework by first proposing a novel sense-aware cross entropy loss to model word senses explicitly.
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.
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.
Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model
Experimental results show that our proposed method achieves significant performance improvements over the state-of-the-art pretrained cross-lingual language model in the CLCD setting.
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks
Data augmentation techniques have been widely used to improve machine learning performance as they enhance the generalization capability of models.
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.
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
With the help of these strategies, we are able to train a model with fewer parameters while maintaining the model capacity.
Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans
Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features.
Position-Aware Tagging for Aspect Sentiment Triplet Extraction
Our observation is that the three elements within a triplet are highly related to each other, and this motivates us to build a joint model to extract such triplets using a sequence tagging approach.
Ranked #3 on
Aspect Sentiment Triplet Extraction
on SemEval
Partially-Aligned Data-to-Text Generation with Distant Supervision
This kind of data is much easier to obtain since it can be produced automatically.
An Unsupervised Sentence Embedding Method by Mutual Information Maximization
However, SBERT is trained on corpus with high-quality labeled sentence pairs, which limits its application to tasks where labeled data is extremely scarce.
Ranked #16 on
Semantic Textual Similarity
on STS16
Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training
To improve the robustness of self-training, in this paper we present class-aware feature self-distillation (CFd) to learn discriminative features from PrLMs, in which PrLM features are self-distilled into a feature adaptation module and the features from the same class are more tightly clustered.
Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling
Exploiting sentence-level labels, which are easy to obtain, is one of the plausible methods to improve low-resource named entity recognition (NER), where token-level labels are costly to annotate.
Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce
We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language.
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph
Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description.
Ranked #1 on
KG-to-Text Generation
on ENT-DESC
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.
GRET: Global Representation Enhanced Transformer
The encoder maps the words in the input sentence into a sequence of hidden states, which are then fed into the decoder to generate the output sentence.
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.
Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis
In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE).
Ranked #5 on
Aspect Sentiment Triplet Extraction
on SemEval
Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning
Customers ask questions and customer service staffs answer their questions, which is the basic service model via multi-turn customer service (CS) dialogues on E-commerce platforms.
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis
Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis.
Ranked #2 on
Emotion Cause Extraction
on ECE
Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations
Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario.
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning
Joint extraction of aspects and sentiments can be effectively formulated as a sequence labeling problem.
Aspect-Based Sentiment Analysis (ABSA)
Unsupervised Domain Adaptation
Improving Question Generation With to the Point Context
Question generation (QG) is the task of generating a question from a reference sentence and a specified answer within the sentence.
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.
Semi-supervised Text Style Transfer: Cross Projection in Latent Space
Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data.
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.
Hierarchical Pointer Net Parsing
Transition-based top-down parsing with pointer networks has achieved state-of-the-art results in multiple parsing tasks, while having a linear time complexity.
An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction
For further exploiting the power of extraction and retrieval, we propose a neural-based merging module to combine and re-rank the predicted keyphrases from the enhanced generative model, the extractive model, and the retrieved keyphrases.
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.
Abstractive Text Summarization by Incorporating Reader Comments
To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect.
Ranked #1 on
Reader-Aware Summarization
on RASG
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
QuaSE: Sequence Editing under Quantifiable Guidance
For example, an input sequence could be a word sequence, such as review sentence and advertisement text.
Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates
Inferring the agreement/disagreement relation in debates, especially in online debates, is one of the fundamental tasks in argumentation mining.
Estimating Marginal Probabilities of n-grams for Recurrent Neural Language Models
Recurrent neural network language models (RNNLMs) are the current standard-bearer for statistical language modeling.
Variational Autoregressive Decoder for Neural Response Generation
Combining the virtues of probability graphic models and neural networks, Conditional Variational Auto-encoder (CVAE) has shown promising performance in applications such as response generation.
Generating Distractors for Reading Comprehension Questions from Real Examinations
We investigate the task of distractor generation for multiple choice reading comprehension questions from examinations.
Difficulty Controllable Generation of Reading Comprehension Questions
We investigate the difficulty levels of questions in reading comprehension datasets such as SQuAD, and propose a new question generation setting, named Difficulty-controllable Question Generation (DQG).
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.
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 Sub Task 2
(Laptop (Acc) metric)
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.
Semi-Supervised Learning with Declaratively Specified Entropy Constraints
We propose a technique for declaratively specifying strategies for semi-supervised learning (SSL).
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.
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.
Recurrent Attention Network on Memory for Aspect Sentiment Analysis
We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review.
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.
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.
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
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.
Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning
We propose a general approach to modeling semi-supervised learning (SSL) algorithms.
Bootstrapping Distantly Supervised IE using Joint Learning and Small Well-structured Corpora
We propose a framework to improve performance of distantly-supervised relation extraction, by jointly learning to solve two related tasks: concept-instance extraction and relation extraction.
Distant IE by Bootstrapping Using Lists and Document Structure
Distant labeling for information extraction (IE) suffers from noisy training data.
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.
Reader-Aware Multi-Document Summarization via Sparse Coding
We propose a new MDS paradigm called reader-aware multi-document summarization (RA-MDS).
