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Found 61 papers, 24 papers with code
Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training
Instead of adopting the classic student-teacher learning of forcing the output of a student network to exactly mimic the soft targets produced by the teacher networks, we introduce two discriminators as in generative adversarial network (GAN) to transfer knowledge from two teachers to the student.
Ranked #5 on
Task-Oriented Dialogue Systems
on KVRET
Generative Adversarial Network
Task-Oriented Dialogue Systems
Wasserstein Selective Transfer Learning for Cross-domain Text Mining
However, the source and target domains usually have different data distributions, which may lead to negative transfer.
Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation
Emotional Support Conversation (ESC) systems are pivotal in providing empathetic interactions, aiding users through negative emotional states by understanding and addressing their unique experiences.
Many-to-many Image Generation with Auto-regressive Diffusion Models
This paper introduces a domain-general framework for many-to-many image generation, capable of producing interrelated image series from a given set of images, offering a scalable solution that obviates the need for task-specific solutions across different multi-image scenarios.
ChartThinker: A Contextual Chain-of-Thought Approach to Optimized Chart Summarization
Data visualization serves as a critical means for presenting data and mining its valuable insights.
A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems
This survey provides a comprehensive review of research on multi-turn dialogue systems, with a particular focus on multi-turn dialogue systems based on large language models (LLMs).
Multimodal Instruction Tuning with Conditional Mixture of LoRA
Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in diverse tasks across different domains, with an increasing focus on improving their zero-shot generalization capabilities for unseen multimodal tasks.
Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning
Despite vision-language models' (VLMs) remarkable capabilities as versatile visual assistants, two substantial challenges persist within the existing VLM frameworks: (1) lacking task diversity in pretraining and visual instruction tuning, and (2) annotation error and bias in GPT-4 synthesized instruction tuning data.
Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework
Chinese Spelling Correction (CSC) aims to detect and correct spelling errors in given sentences.
On the Convergence of Zeroth-Order Federated Tuning for Large Language Models
The confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering in a new era in privacy-preserving natural language processing.
Enhancing Multimodal Large Language Models with Vision Detection Models: An Empirical Study
Despite the impressive capabilities of Multimodal Large Language Models (MLLMs) in integrating text and image modalities, challenges remain in accurately interpreting detailed visual elements.
Ranked #37 on
Visual Question Answering
on MM-Vet
Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters
To the best of our knowledge, Visual-C$^3$ is the first real-world visual and the largest human-crafted dataset for the Chinese character checking scenario.
X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects
Natural Language Generation (NLG) typically involves evaluating the generated text in various aspects (e. g., consistency and naturalness) to obtain a comprehensive assessment.
Tunable Soft Prompts are Messengers in Federated Learning
As the global model itself is not required to be shared and the local training is conducted based on an auxiliary model with fewer parameters than the global model, the proposed approach provides protection for the global model while reducing communication and computation costs in FL.
MULTISCRIPT: Multimodal Script Learning for Supporting Open Domain Everyday Tasks
Automatically generating scripts (i. e. sequences of key steps described in text) from video demonstrations and reasoning about the subsequent steps are crucial to the modern AI virtual assistants to guide humans to complete everyday tasks, especially unfamiliar ones.
Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correction
During the training process, ProTEC guides the model to learn text error correction by incorporating these sub-tasks into a progressive paradigm.
Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking
Entity Linking (EL) is a fundamental task for Information Extraction and Knowledge Graphs.
LTCR: Long-Text Chinese Rumor Detection Dataset
False information can spread quickly on social media, negatively influencing the citizens' behaviors and responses to social events.
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.
The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models
Chain-of-Thought (CoT) prompting enables large language models to solve complex reasoning problems by generating intermediate steps.
Counterfactual Debiasing for Generating Factually Consistent Text Summaries
Despite substantial progress in abstractive text summarization to generate fluent and informative texts, the factual inconsistency in the generated summaries remains an important yet challenging problem to be solved.
CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction
Evaluating the performance of Grammatical Error Correction (GEC) systems is a challenging task due to its subjectivity.
From Retrieval to Generation: Efficient and Effective Entity Set Expansion
For efficiency, expansion time consumed by GenExpan is independent of entity vocabulary and corpus size, and GenExpan achieves an average 600% speedup compared to strong baselines.
Learning by Asking for Embodied Visual Navigation and Task Completion
The research community has shown increasing interest in designing intelligent embodied agents that can assist humans in accomplishing tasks.
MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning
Our results indicate that fine-tuning the model on a diverse set of tasks and instructions leads to a reduced sensitivity to variations in instructions for each task.
Collaborating Heterogeneous Natural Language Processing Tasks via Federated Learning
In this study, we further broaden the application scope of FL in NLP by proposing an Assign-Then-Contrast (denoted as ATC) framework, which enables clients with heterogeneous NLP tasks to construct an FL course and learn useful knowledge from each other.
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction
Open Relation Extraction (OpenRE) aims to discover novel relations from open domains.
Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation
Facing this dilemma, we focus on a novel CTG scenario, i. e., blessing generation which is challenging because high-quality blessing texts require CTG models to comprehensively consider the entanglement between multiple attributes (e. g., objects and occasions).
Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction
Extensive experiments and detailed analyses not only demonstrate that the training data constructed by our method effectively improves the performance of CGEC models, but also reflect that our benchmark is an excellent resource for further development of the CGEC field.
Automatic Context Pattern Generation for Entity Set Expansion
In addition, we propose the GAPA, a novel ESE framework that leverages the aforementioned GenerAted PAtterns to expand target entities.
Contrastive Learning with Hard Negative Entities for Entity Set Expansion
In addition, we propose the ProbExpan, a novel probabilistic ESE framework utilizing the entity representation obtained by the aforementioned language model to expand entities.
Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model
Depression is a global mental health problem, the worst case of which can lead to suicide.
A Survey of Natural Language Generation
This paper offers a comprehensive review of the research on Natural Language Generation (NLG) over the past two decades, especially in relation to data-to-text generation and text-to-text generation deep learning methods, as well as new applications of NLG technology.
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression
In this paper, we target to compress PLMs with knowledge distillation, and propose a hierarchical relational knowledge distillation (HRKD) method to capture both hierarchical and domain relational information.
Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking
This ability to learn consecutive tasks without forgetting how to perform previously trained problems is essential for developing an online dialogue system.
Imagine, Reason and Write: Visual Storytelling with Graph Knowledge and Relational Reasoning
Finally, we integrate the imaginary concepts and relational knowledge to generate human-like story based on the original semantics of images.
Ranked #2 on
Visual Storytelling
on VIST
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.
Prototypical Representation Learning for Relation Extraction
This approach allows us to learn meaningful, interpretable prototypes for the final classification.
Learning Purified Feature Representations from Task-irrelevant Labels
Learning an empirically effective model with generalization using limited data is a challenging task for deep neural networks.
Learning to Augment for Data-Scarce Domain BERT Knowledge Distillation
Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of natural language processing tasks, they are computationally expensive to be deployed in real-time applications.
Answer-driven Deep Question Generation based on Reinforcement Learning
First, we propose an answer-aware initialization module with a gated connection layer which introduces both document and answer information to the decoder, thus helping to guide the choice of answer-focused question words.
Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition
Local dependencies, which captures short-term emotional effects between neighbouring utterances, are further injected via an Aggregation Graph to distinguish the subtle differences between utterances containing emotional phrases.
Ranked #28 on
Emotion Recognition in Conversation
on IEMOCAP
Integrating User History into Heterogeneous Graph for Dialogue Act Recognition
Dialogue Act Recognition (DAR) is a challenging problem in Natural Language Understanding, which aims to attach Dialogue Act (DA) labels to each utterance in a conversation.
Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels
We also provide a theoretical explanation of our method.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents
Distant supervision based methods for entity and relation extraction have received increasing popularity due to the fact that these methods require light human annotation efforts.
A multi-agent ontologies-based clinical decision support system
Our approach is based on the specialization of agents adapted to the knowledge models used during the clinical steps and ontologies.
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.
Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge
Chinese relation extraction is conducted using neural networks with either character-based or word-based inputs, and most existing methods typically suffer from segmentation errors and ambiguity of polysemy.
Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering
Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection.
Improving Medical Short Text Classification with Semantic Expansion Using Word-Cluster Embedding
However, in electronic medical records (EMR), the texts containing sentences are shorter than that in general domain, which leads to the lack of semantic features and the ambiguity of semantic.
Approach for Semi-automatic Construction of Anti-infective Drug Ontology Based on Entity Linking
Experiments show that our selected features have achieved a precision rate of 86. 77%, a recall rate of 89. 03% and an F1 score of 87. 89%.
Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning
Disease Ontology (DO) that pertains to cancer's clinical stages and their corresponding information components is utilized to improve the reasoning ability of a decision support system (DSS).
A Knowledge Graph Based Solution for Entity Discovery and Linking in Open-Domain Questions
In Question Entity Discovery and Linking (QEDL) problem, traditional methods are challenged because multiple entities in one short question are difficult to be discovered entirely and the incomplete information in short text makes entity linking hard to implement.
MedSim: A Novel Semantic Similarity Measure in Bio-medical Knowledge Graphs
We present MedSim, a novel semantic SIMilarity method based on public well-established bio-MEDical knowledge graphs (KGs) and large-scale corpus, to study the therapeutic substitution of antibiotics.
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication.
MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data
In the light of these challenges, we propose a new truth discovery method, MedTruth, for medical knowledge condition discovery, which incorporates prior source quality information into the source reliability estimation procedure, and also utilizes the knowledge triple information for trustworthy information computation.
Databases
Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge
In this paper, we propose Knowledge-aware Attentive Network (KAN), a transfer learning framework for cross-domain answer selection, which uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domains.
Aspect and Sentiment Aware Abstractive Review Summarization
Review text has been widely studied in traditional tasks such as sentiment analysis and aspect extraction.
Cooperative Denoising for Distantly Supervised Relation Extraction
Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts.
Efficient Low-rank Multimodal Fusion with Modality-Specific Factors
Previous research in this field has exploited the expressiveness of tensors for multimodal representation.
VSI: A Visual Saliency-Induced Index for Perceptual Image Quality Assessment
First, VS is used as a feature when computing the local quality map of the distorted image.
Ranked #7 on
Video Quality Assessment
on MSU FR VQA Database
