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Found 344 papers, 158 papers with code
Challenges to Open-Domain Constituency Parsing
Neural constituency parsers have reached practical performance on news-domain benchmarks.
PromptGen: Automatically Generate Prompts using Generative Models
Recently, prompt learning has received significant attention, where the downstream tasks are reformulated to the mask-filling task with the help of a textual prompt.
Inducing Target-Specific Latent Structures for Aspect Sentiment Classification
Aspect-level sentiment analysis aims to recognize the sentiment polarity of an aspect or a target in a comment.
Entity Enhanced BERT Pre-training for Chinese NER
We then integrate the entity information into BERT using Char-Entity-Transformer, which augments the self-attention using a combination of character and entity representations.
Natural Language Processing Meets Quantum Physics: A Survey and Categorization
Recent research has investigated quantum NLP, designing algorithms that process natural language in quantum computers, and also quantum-inspired algorithms that improve NLP performance on classical computers.
Speeding up Transformer Decoding via an Attention Refinement Network
Extensive experiments on ten WMT machine translation tasks show that the proposed model yields an average of 1. 35x faster (with almost no decrease in BLEU) over the state-of-the-art inference implementation.
Learning to Rank in the Age of Muppets: Effectiveness–Efficiency Tradeoffs in Multi-Stage Ranking
It is well known that rerankers built on pretrained transformer models such as BERT have dramatically improved retrieval effectiveness in many tasks.
Investigating Rich Feature Sources for Conceptual Representation Encoding
Functional Magnetic Resonance Imaging (fMRI) provides a means to investigate human conceptual representation in cognitive and neuroscience studies, where researchers predict the fMRI activations with elicited stimuli inputs.
Cross-Lingual Dependency Parsing via Self-Training
Recent advances of multilingual word representations weaken the input divergences across languages, making cross-lingual transfer similar to the monolingual cross-domain and semi-supervised settings.
新型冠状病毒肺炎相关的推特主题与情感研究(Exploring COVID-19-related Twitter Topic Dynamics across Countries)
我们基于从2020年1月22日至2020年4月30日在推特社交平台上抓取的不同国家和地区发布的50万条推文, 研究了有关 2019新型冠状病毒肺炎相关的主题和人们的观点, 发现了不同国家之间推特用户的普遍关切和看法之间存在着异同, 并且对不同议题的情感态度也有所不同。我们发现大部分推文中包含了强烈的情感, 其中表达爱与支持的推文比较普遍。总体来看, 人们的情感随着时间的推移逐渐正向增强。
Discrete Opinion Tree Induction for Aspect-based Sentiment Analysis
Dependency trees have been intensively used with graph neural networks for aspect-based sentiment classification.
Aspect-Based Sentiment Analysis (ABSA)
Sentiment Classification
Explicit Object Relation Alignment for Vision and Language Navigation
In this paper, we investigate the problem of vision and language navigation.
Contrastive Data and Learning for Natural Language Processing
We then survey the benefits and the best practices of contrastive learning for various downstream NLP applications including Text Classification, Question Answering, Summarization, Text Generation, Interpretability and Explainability, Commonsense Knowledge and Reasoning, Vision-and-Language. This tutorial intends to help researchers in the NLP and computational linguistics community to understand this emerging topic and promote future research directions of using contrastive learning for NLP applications.
DialogSum Challenge: Summarizing Real-Life Scenario Dialogues
We propose a shared task on summarizing real-life scenario dialogues, DialogSum Challenge, to encourage researchers to address challenges in dialogue summarization, which has been less studied by the summarization community.
Prompt-Driven Neural Machine Translation
Neural machine translation (NMT) has obtained significant performance improvement over the recent years.
Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities
This paper presents a comprehensive survey on integrating reinforcement learning into the evolutionary algorithm, referred to as reinforcement learning-assisted evolutionary algorithm (RL-EA).
An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
Moreover, we find that ALPACA can maintain more knowledge and capacity compared with LLAMA during the continual fine-tuning, which implies that general instruction tuning can help mitigate the forgetting phenomenon of LLMs in the further fine-tuning process.
DPMix: Mixture of Depth and Point Cloud Video Experts for 4D Action Segmentation
The proposed method, named Mixture of Depth and Point cloud video experts (DPMix), achieved the first place in the 4D Action Segmentation Track of the HOI4D Challenge 2023.
Multi-representations Space Separation based Graph-level Anomaly-aware Detection
The previous works have observed that abnormal graphs mainly show node-level and graph-level anomalies, but these methods equally treat two anomaly forms above in the evaluation of abnormal graphs, which is contrary to the fact that different types of abnormal graph data have different degrees in terms of node-level and graph-level anomalies.
Zero-shot Query Reformulation for Conversational Search
Nevertheless, existing zero-shot methods face three primary limitations: they are not universally applicable to all retrievers, their effectiveness lacks sufficient explainability, and they struggle to resolve common conversational ambiguities caused by omission.
An Exploration Study of Mixed-initiative Query Reformulation in Conversational Passage Retrieval
In this work, we aim to reproduce multi-stage retrieval pipelines and explore one of the potential benefits of involving mixed-initiative interaction in conversational passage retrieval scenarios: reformulating raw queries.
ConTrack: Contextual Transformer for Device Tracking in X-ray
Tracking the catheter tip poses different challenges: the tip can be occluded by contrast during angiography or interventional devices; and it is always in continuous movement due to the cardiac and respiratory motions.
TVPR: Text-to-Video Person Retrieval and a New Benchmark
To the best of our knowledge, TVPRN is the first successful attempt to use video for text-based person retrieval task and has achieved state-of-the-art performance on TVPReid dataset.
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation
Additionally, based on the same intuition, we propose a 2-Step method, which takes both conversation and summary as input to simulate human annotation process.
A Survey on Evaluation of Large Language Models
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications.
Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model
We also show that pretraining the QE module with auto-generated QE data from user logs can further improve the overall performance.
Explicit Syntactic Guidance for Neural Text Generation
Most existing text generation models follow the sequence-to-sequence paradigm.
Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations
Discourse analysis is an important task because it models intrinsic semantic structures between sentences in a document.
Opinion Tree Parsing for Aspect-based Sentiment Analysis
To address these challenges, we propose an opinion tree parsing model, aiming to parse all the sentiment elements from an opinion tree, which is much faster, and can explicitly reveal a more comprehensive and complete aspect-level sentiment structure.
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
To ensure the reliability of PandaLM, we collect a diverse human-annotated test dataset, where all contexts are generated by humans and labels are aligned with human preferences.
PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
The increasing reliance on Large Language Models (LLMs) across academia and industry necessitates a comprehensive understanding of their robustness to prompts.
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction
Motivated by the fact that all event structures can be inferred from AMR, this work reformulates EAE as a link prediction problem on AMR graphs.
RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering
Open-Domain Question Answering (ODQA) systems necessitate a reader model capable of generating answers by simultaneously referring to multiple passages.
Exploiting Abstract Meaning Representation for Open-Domain Question Answering
The Open-Domain Question Answering (ODQA) task involves retrieving and subsequently generating answers from fine-grained relevant passages within a database.
NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts
We introduce NaSGEC, a new dataset to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains.
Out-of-Distribution Generalization in Text Classification: Past, Present, and Future
Machine learning (ML) systems in natural language processing (NLP) face significant challenges in generalizing to out-of-distribution (OOD) data, where the test distribution differs from the training data distribution.
EASE: An Easily-Customized Annotation System Powered by Efficiency Enhancement Mechanisms
Our results show that our system can meet the diverse needs of NLP researchers and significantly accelerate the annotation process.
Non-Autoregressive Document-Level Machine Translation (NA-DMT): Exploring Effective Approaches, Challenges, and Opportunities
Non-autoregressive translation (NAT) models have been extensively investigated within the context of sentence-level machine translation (MT) tasks, demonstrating comparable quality and superior translation speed when contrasted with autoregressive translation (AT) models.
Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance
ChatGPT and GPT-4 have attracted substantial interest from both academic and industrial circles, owing to their remarkable few-shot (or even zero-shot) ability to handle various tasks.
Deepfake Text Detection in the Wild
In practical scenarios, the detector faces texts from various domains or LLMs without knowing their sources.
Evaluating Open-QA Evaluation
This study focuses on the evaluation of the Open Question Answering (Open-QA) task, which can directly estimate the factuality of large language models (LLMs).
LogiCoT: Logical Chain-of-Thought Instruction-Tuning Data Collection with GPT-4
LogiCoT serves as an instruction set for teaching models of logical reasoning and elicits general reasoning skills.
Mitigating Catastrophic Forgetting in Task-Incremental Continual Learning with Adaptive Classification Criterion
It is seldom considered to adapt the decision boundary for new representations and in this paper we propose a Supervised Contrastive learning framework with adaptive classification criterion for Continual Learning (SCCL), In our method, a contrastive loss is used to directly learn representations for different tasks and a limited number of data samples are saved as the classification criterion.
ALT: An Automatic System for Long Tail Scenario Modeling
In this paper, we consider the problem of long tail scenario modeling with budget limitation, i. e., insufficient human resources for model training stage and limited time and computing resources for model inference stage.
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.
Measuring Consistency in Text-based Financial Forecasting Models
Using FinTrust, we show that the consistency of state-of-the-art NLP models for financial forecasting is poor.
Learning to Generalize for Cross-domain QA
Our method has been theoretically and empirically shown to be effective in enhancing the generalization ability of both generative and discriminative models.
Temporal Consistent Automatic Video Colorization via Semantic Correspondence
Extensive experiments demonstrate that our method outperforms other methods in maintaining temporal consistency both qualitatively and quantitatively.
Instance Smoothed Contrastive Learning for Unsupervised Sentence Embedding
Contrastive learning-based methods, such as unsup-SimCSE, have achieved state-of-the-art (SOTA) performances in learning unsupervised sentence embeddings.
Investigating Forgetting in Pre-Trained Representations Through Continual Learning
Intuitively, the representation forgetting can influence the general knowledge stored in pre-trained language models (LMs), but the concrete effect is still unclear.
Token-Level Fitting Issues of Seq2seq Models
Sequence-to-sequence (seq2seq) models have been widely used for natural language processing, computer vision, and other deep learning tasks.
Target-Side Augmentation for Document-Level Machine Translation
Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data, increasing the risk of learning spurious patterns.
A Curriculum View of Robust Loss Functions
Robust loss functions are designed to combat the adverse impacts of label noise, whose robustness is typically supported by theoretical bounds agnostic to the training dynamics.
Optimizing Energy Efficiency in Metro Systems Under Uncertainty Disturbances Using Reinforcement Learning
In the realm of urban transportation, metro systems serve as crucial and sustainable means of public transit.
Tailored Multi-Organ Segmentation with Model Adaptation and Ensemble
Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis.
SPColor: Semantic Prior Guided Exemplar-based Image Colorization
Previous methods search for correspondence across the entire reference image, and this type of global matching is easy to get mismatch.
Unified Multi-Modal Image Synthesis for Missing Modality Imputation
Besides, we propose a Dynamic Feature Unification Module to integrate information from a varying number of available modalities, which enables the network to be robust to random missing modalities.
GEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization
Human experts write summaries using different techniques, including rewriting a sentence in the document or fusing multiple sentences to generate a summary sentence.
Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4
With the release of Generative Pretrained Transformer 4 (GPT-4), highlighted as "advanced" at reasoning tasks, we are eager to learn the GPT-4 performance on various logical reasoning tasks.
Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation
To showcase its capabilities in GEC, we design zero-shot chain-of-thought (CoT) and few-shot CoT settings using in-context learning for ChatGPT.
Exemplar-based Video Colorization with Long-term Spatiotemporal Dependency
Exemplar-based video colorization is an essential technique for applications like old movie restoration.
POAR: Towards Open Vocabulary Pedestrian Attribute Recognition
Our key idea is to formulate the POAR problem as an image-text search problem.
RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation
It streamlines the repository-level code completion process by incorporating a similarity-based retriever and a pre-trained code language model, which allows for the effective utilization of repository-level information for code completion and grants the ability to generate code at various levels of granularity.
Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism
Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty. Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations.
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective.
VLN-Trans: Translator for the Vision and Language Navigation Agent
The mentioned landmarks are not recognizable by the navigation agent due to the different vision abilities of the instructor and the modeled agent.
GLUECons: A Generic Benchmark for Learning Under Constraints
Recent research has shown that integrating domain knowledge into deep learning architectures is effective -- it helps reduce the amount of required data, improves the accuracy of the models' decisions, and improves the interpretability of models.
Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions
(Dis)agreement detection aims to identify the authors' attitudes or positions (\textit{{agree, disagree, neutral}}) towards a specific text.
Uniform tensor clustering by jointly exploring sample affinities of various orders
Conventional clustering methods based on pairwise affinity usually suffer from the concentration effect while processing huge dimensional features yet low sample sizes data, resulting in inaccuracy to encode the sample proximity and suboptimal performance in clustering.
Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding
To evaluate the effectiveness, we perform multi-level difficulty part language grounding grasping experiments and deploy our proposed model on a real robot.
Exploiting Rich Textual User-Product Context for Improving Sentiment Analysis
User and product information associated with a review is useful for sentiment polarity prediction.
DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding
In this work, we show that the issue arises from the un-consistency of label smoothing on the token-level and sequence-level distributions.
Ranking Aggregation with Interactive Feedback for Collaborative Person Re-identification
Existing RA work can be generally divided into unsupervised methods and fully-supervised methods.
UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization
The high annotation costs and diverse demands of various summarization tasks motivate the development of few-shot summarization.
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective
Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase.
Natural Language Understanding
Out-of-Distribution Generalization
CSynGEC: Incorporating Constituent-based Syntax for Grammatical Error Correction with a Tailored GEC-Oriented Parser
Recently, Zhang et al. (2022) propose a syntax-aware grammatical error correction (GEC) approach, named SynGEC, showing that incorporating tailored dependency-based syntax of the input sentence is quite beneficial to GEC.
RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees
RLET iteratively performs single step reasoning with sentence selection and deduction generation modules, from which the training signal is accumulated across the tree with elaborately designed aligned reward function that is consistent with the evaluation.
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings
Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e. g., Skip-gram, Word2Vec) still play an important role in low-resource and lightweight settings due to their low computational cost, ease of deployment, and stability.
Cross-domain Generalization for AMR Parsing
Based on our observation, we investigate two approaches to reduce the domain distribution divergence of text and AMR features, respectively.
SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser
Then, we obtain parse trees of the source incorrect sentences by projecting trees of the target correct sentences.
Multi-Granularity Optimization for Non-Autoregressive Translation
Despite low latency, non-autoregressive machine translation (NAT) suffers severe performance deterioration due to the naive independence assumption.
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP).
Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models
In particular, we focus on few-shot image recognition tasks on pretrained vision-language models (PVLMs) and develop a method of prompting through prototype (PTP), where we define $K$ image prototypes and $K$ prompt prototypes.
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing
Specifically, we build a noise model to estimate the unknown labeling noise distribution over input contexts and noisy type labels.
Categorizing Semantic Representations for Neural Machine Translation
Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks.
LOViS: Learning Orientation and Visual Signals for Vision and Language Navigation
Understanding spatial and visual information is essential for a navigation agent who follows natural language instructions.
Semantic-based Pre-training for Dialogue Understanding
However, these models are typically trained on surface dialogue text, thus are proven to be weak in understanding the main semantic meaning of a dialogue context.
Can Offline Reinforcement Learning Help Natural Language Understanding?
Experimental results show that our RL pre-trained models can give close performance compared with the models using the LM training objective, showing that there exist common useful features across these two modalities.
Pre-Training a Graph Recurrent Network for Language Representation
Transformer-based pre-trained models have gained much advance in recent years, becoming one of the most important backbones in natural language processing.
FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition
Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource domains and thus received proper attention in recent years.
Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect
Text-to-SQL has attracted attention from both the natural language processing and database communities because of its ability to convert the semantics in natural language into SQL queries and its practical application in building natural language interfaces to database systems.
Lost in Context? On the Sense-wise Variance of Contextualized Word Embeddings
We quantify how much the contextualized embeddings of each word sense vary across contexts in typical pre-trained models.
Mere Contrastive Learning for Cross-Domain Sentiment Analysis
Cross-domain sentiment analysis aims to predict the sentiment of texts in the target domain using the model trained on the source domain to cope with the scarcity of labeled data.
Open Information Extraction from 2007 to 2022 -- A Survey
Open information extraction is an important NLP task that targets extracting structured information from unstructured text without limitations on the relation type or the domain of the text.
Exploiting Sentiment and Common Sense for Zero-shot Stance Detection
Meanwhile, ablation studies prove the significance of each module in our model.
Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction
Limited labeled data increase the risk of distribution shift between test data and training data.
USB: A Unified Semi-supervised Learning Benchmark for Classification
We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
Dual Domain-Adversarial Learning for Audio-Visual Saliency Prediction
The domain discrepancy induces to performance degradation on target testing data for CNN models.
DialogSum Challenge: Results of the Dialogue Summarization Shared Task
We report the results of DialogSum Challenge, the shared task on summarizing real-life scenario dialogues at INLG 2022.
Modeling mandatory and discretionary lane changes using dynamic interaction networks
A quantitative understanding of dynamic lane-changing (LC) interaction patterns is indispensable for improving the decision-making of autonomous vehicles, especially in mixed traffic with human-driven vehicles.
A General Contextualized Rewriting Framework for Text Summarization
The rewriting method for text summarization combines extractive and abstractive approaches, improving the conciseness and readability of extractive summaries using an abstractive model.
Mining Error Templates for Grammatical Error Correction
We have accumulated 1, 119 error templates for Chinese GEC based on this method.
A Graph Enhanced BERT Model for Event Prediction
To this end, we incorporate an additional structured variable into BERT to learn to predict the event connections in the training process.
The Cross-lingual Conversation Summarization Challenge
We propose the shared task of cross-lingual conversation summarization, \emph{ConvSumX Challenge}, opening new avenues for researchers to investigate solutions that integrate conversation summarization and machine translation.
Abstractive Dialogue Summarization
Cross-Lingual Abstractive Summarization
+3
An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random Forest
Evidence from observational studies has become increasingly important for supporting healthcare policy making via cost-effectiveness (CE) analyses.
MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction
This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7, 063 sentences collected from three Chinese-as-a-Second-Language (CSL) learner sources.
On Effectively Learning of Knowledge in Continual Pre-training
Pre-trained language models (PLMs) like BERT have made significant progress in various downstream NLP tasks.
On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART
Word ordering is a constrained language generation task taking unordered words as input.
Towards Fine-grained Causal Reasoning and QA
Understanding causality is key to the success of NLP applications, especially in high-stakes domains.
Challenges for Open-domain Targeted Sentiment Analysis
Since previous studies on open-domain targeted sentiment analysis are limited in dataset domain variety and sentence level, we propose a novel dataset consisting of 6, 013 human-labeled data to extend the data domains in topics of interest and document level.
A Rationale-Centric Framework for Human-in-the-loop Machine Learning
We present a novel rationale-centric framework with human-in-the-loop -- Rationales-centric Double-robustness Learning (RDL) -- to boost model out-of-distribution performance in few-shot learning scenarios.
Multi-Scale Context-Guided Lumbar Spine Disease Identification with Coarse-to-fine Localization and Classification
Accurate and efficient lumbar spine disease identification is crucial for clinical diagnosis.
Graph Pre-training for AMR Parsing and Generation
To our knowledge, we are the first to consider pre-training on semantic graphs.
Ranked #1 on
AMR-to-Text Generation
on Bio
(BLEU metric, using extra
training data)
Towards Robust Online Dialogue Response Generation
Although pre-trained sequence-to-sequence models have achieved great success in dialogue response generation, chatbots still suffer from generating inconsistent responses in real-world practice, especially in multi-turn settings.
Do Prompts Solve NLP Tasks Using Natural Language?
Thanks to the advanced improvement of large pre-trained language models, prompt-based fine-tuning is shown to be effective on a variety of downstream tasks.
AdaPrompt: Adaptive Model Training for Prompt-based NLP
However, for prompt learning, there are still two salient gaps between NLP tasks and pretraining.
Revisiting QMIX: Discriminative Credit Assignment by Gradient Entropy Regularization
In cooperative multi-agent systems, agents jointly take actions and receive a team reward instead of individual rewards.
NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting
Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Nonautoregressive Encoder-Decoder Neural Framework for End-to-End Aspect-Based Sentiment Triplet Extraction
Aspect-based sentiment triplet extraction (ASTE) aims at recognizing the joint triplets from texts, i. e., aspect terms, opinion expressions, and correlated sentiment polarities.
Ranked #3 on
Aspect Sentiment Triplet Extraction
on ASTE-Data-V2
whu-nercms at trecvid2021:instance search task
We will make a brief introduction of the experimental methods and results of the WHU-NERCMS in the TRECVID2021 in the paper.
Confidence-Aware Active Feedback for Interactive Instance Search
To address this issue, we propose a confidence-aware active feedback method (CAAF) that is specifically designed for online RF in interactive INS tasks.
Entity Relation Extraction as Dependency Parsing in Visually Rich Documents
Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i. e., semantic entity), while the relations in-between are largely unexplored.
Ranked #2 on
Entity Linking
on FUNSD
PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation
3D shape representation and its processing have substantial effects on 3D shape recognition.
Ranked #1 on
3D Object Classification
on ModelNet10
Solving Aspect Category Sentiment Analysis as a Text Generation Task
Aspect category sentiment analysis has attracted increasing research attention.
NAIL: A Challenging Benchmark for Na\"ive Logical Reasoning
Logical reasoning over natural text is an important capability towards human level intelligence.
Investigating Non-local Features for Neural Constituency Parsing
Besides, our method achieves state-of-the-art BERT-based performance on PTB (95. 92 F1) and strong performance on CTB (92. 31 F1).
Ranked #6 on
Constituency Parsing
on Penn Treebank
Automatic hippocampal surface generation via 3D U-net and active shape modeling with hybrid particle swarm optimization
The hippocampal surface was then generated from the mean shape and the shape variation parameters.
Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation
To deal with this problem, instead of introducing knowledge base as the input, we force the model to learn a better semantic representation by predicting the information in the knowledge base, only based on the input context.
Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation
Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR.
Exploring Generalization Ability of Pretrained Language Models on Arithmetic and Logical Reasoning
To quantitatively and intuitively explore the generalization ability of pre-trained language models (PLMs), we have designed several tasks of arithmetic and logical reasoning.
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting
In this work, we propose a novel LiTS method to adequately aggregate multi-phase information and refine uncertain region segmentation.
End-to-End AMR Coreference Resolution
Although parsing to Abstract Meaning Representation (AMR) has become very popular and AMR has been shown effective on the many sentence-level downstream tasks, little work has studied how to generate AMRs that can represent multi-sentence information.
Understanding the merging behavior patterns and evolutionary mechanism at freeway on-ramps
This study develops a primitive-based framework to identify the driving patterns during merging processes and reveal the evolutionary mechanism at freeway on-ramps in congested traffic flow.
Supervised Off-Policy Ranking
Inspired by the two observations, in this work, we study a new problem, supervised off-policy ranking (SOPR), which aims to rank a set of target policies based on supervised learning by leveraging off-policy data and policies with known performance.
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis
While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustness and their underlying sensitivity to systematic biases that may exist in their training and test data.
Non-Point Visible Light Transmitter Localization based on Monocular Camera
Many algorithms for visible light positioning (VLP) localization do not consider the shapes of the transmitters, which leads to the impracticality of the algorithm and the low localization accuracy.
Template-Based Named Entity Recognition Using BART
To address the issue, we propose a template-based method for NER, treating NER as a language model ranking problem in a sequence-to-sequence framework, where original sentences and statement templates filled by candidate named entity span are regarded as the source sequence and the target sequence, respectively.
Can Generative Pre-trained Language Models Serve as Knowledge Bases for Closed-book QA?
Recent work has investigated the interesting question using pre-trained language models (PLMs) as knowledge bases for answering open questions.
A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents
Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of {``}Who expressed what opinions towards what{''} in one sentence.
G-Transformer for Document-level Machine Translation
However, study shows that when we further enlarge the translation unit to a whole document, supervised training of Transformer can fail.
On Compositional Generalization of Neural Machine Translation
Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks such as WMT.
V2V Spatiotemporal Interactive Pattern Recognition and Risk Analysis in Lane Changes
This study explores the spatiotemporal evolution law and risk formation mechanism of the LC interactive patterns and the findings are useful for comprehensively understanding the latent interactive patterns, improving the rationality and safety of autonomous vehicle's decision-making.
Semantic Representation for Dialogue Modeling
Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities.
Ranked #8 on
Dialog Relation Extraction
on DialogRE
Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter
Lexicon information and pre-trained models, such as BERT, have been combined to explore Chinese sequence labelling tasks due to their respective strengths.
Towards Navigation by Reasoning over Spatial Configurations
Additionally, the experimental results demonstrate that explicit modeling of spatial semantic elements in the instructions can improve the grounding and spatial reasoning of the model.
DialogSum: A Real-Life Scenario Dialogue Summarization Dataset
Proposal of large-scale datasets has facilitated research on deep neural models for news summarization.
Abstractive Dialogue Summarization
Common Sense Reasoning
+3
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
To guarantee user acceptability, all the text transformations are linguistically based, and we provide a human evaluation for each one.
Structural Adapters in Pretrained Language Models for AMR-to-text Generation
Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representation.
Ranked #1 on
Data-to-Text Generation
on AMR3.0
Constrained Text Generation with Global Guidance -- Case Study on CommonGen
We focus on CommonGen, the task of generating text based on a set of concepts, as a representative task of constrained text generation.