Search Results for author:
Found 27 papers, 10 papers with code
An Empirical Study of Instruction-tuning Large Language Models in Chinese
This paper will release a powerful Chinese LLMs that is comparable to ChatGLM.
How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View
However, most existing KGE works focus on the design of delicate triple modeling function, which mainly tells us how to measure the plausibility of observed triples, but offers limited explanation of why the methods can extrapolate to unseen data, and what are the important factors to help KGE extrapolate.
Ranked #12 on
Link Prediction
on FB15k-237
Graph Geometry Interaction Learning
To utilize the strength of both Euclidean and hyperbolic geometries, we develop a novel Geometry Interaction Learning (GIL) method for graphs, a well-suited and efficient alternative for learning abundant geometric properties in graph.
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA
To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets.
Task-adaptive Neural Process for User Cold-Start Recommendation
In this paper, we develop a novel meta-learning recommender called task-adaptive neural process (TaNP).
Neutral Utterances are Also Causes: Enhancing Conversational Causal Emotion Entailment with Social Commonsense Knowledge
Conversational Causal Emotion Entailment aims to detect causal utterances for a non-neutral targeted utterance from a conversation.
Ranked #1 on
Causal Emotion Entailment
on RECCON
A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models
In response to the efficiency problem, recent studies show that dense PLMs can be replaced with sparse subnetworks without hurting the performance.
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training
Firstly, we discover that the success of magnitude pruning can be attributed to the preserved pre-training performance, which correlates with the downstream transferability.
Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning
However, these models reveal a trade-off that the improvements on OOD data severely sacrifice the performance on the in-distribution (ID) data (which is dominated by the biased samples).
Towards Better Entity Linking with Multi-View Enhanced Distillation
Aiming at learning entity representations that can match divergent mentions, this paper proposes a Multi-View Enhanced Distillation (MVD) framework, which can effectively transfer knowledge of multiple fine-grained and mention-relevant parts within entities from cross-encoders to dual-encoders.
Joint Entity Linking with Deep Reinforcement Learning
Entity linking is the task of aligning mentions to corresponding entities in a given knowledge base.
Ranked #6 on
Entity Disambiguation
on AIDA-CoNLL
RLINK: Deep Reinforcement Learning for User Identity Linkage
User identity linkage is a task of recognizing the identities of the same user across different social networks (SN).
HIN: Hierarchical Inference Network for Document-Level Relation Extraction
In this paper, we propose a Hierarchical Inference Network (HIN) to make full use of the abundant information from entity level, sentence level and document level.
Ranked #51 on
Relation Extraction
on DocRED
Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network
Sentence-level extractive text summarization is substantially a node classification task of network mining, adhering to the informative components and concise representations.
Ranked #1 on
Extractive Text Summarization
on CNN / Daily Mail
Deep Differential Amplifier for Extractive Summarization
In this paper, we conceptualize the single-document extractive summarization as a rebalance problem and present a deep differential amplifier framework.
Is There More Pattern in Knowledge Graph? Exploring Proximity Pattern for Knowledge Graph Embedding
Modeling of relation pattern is the core focus of previous Knowledge Graph Embedding works, which represents how one entity is related to another semantically by some explicit relation.
TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network
To alleviate label scarcity in Named Entity Recognition (NER) task, distantly supervised NER methods are widely applied to automatically label data and identify entities.
SOM-NCSCM : An Efficient Neural Chinese Sentence Compression Model Enhanced with Self-Organizing Map
Sentence Compression (SC), which aims to shorten sentences while retaining important words that express the essential meanings, has been studied for many years in many languages, especially in English.
Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization
In zero-shot multilingual extractive text summarization, a model is typically trained on English summarization dataset and then applied on summarization datasets of other languages.
Non-Autoregressive Chinese ASR Error Correction with Phonological Training
Automatic Speech Recognition (ASR) is an efficient and widely used input method that transcribes speech signals into text.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Slot Dependency Modeling for Zero-Shot Cross-Domain Dialogue State Tracking
Zero-shot learning for Dialogue State Tracking (DST) focuses on generalizing to an unseen domain without the expense of collecting in domain data.
CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction
Transforming the large amounts of unstructured text on the Internet into structured event knowledge is a critical, yet unsolved goal of NLP, especially when addressing document-level text.
Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking
Zero-shot transfer learning for Dialogue State Tracking (DST) helps to handle a variety of task-oriented dialogue domains without the cost of collecting in-domain data.
DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation
Empathy is a crucial factor in open-domain conversations, which naturally shows one's caring and understanding to others.
Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models
We evaluate our method on both open and closed LLMs, and the experiments on the widely-used public dataset show that our method can generate more consistent responses in a long-context conversation.
Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation
Knowledge-grounded dialogue generation aims to mitigate the issue of text degeneration by incorporating external knowledge to supplement the context.
A Group Fairness Lens for Large Language Models
The rapid advancement of large language models has revolutionized various applications but also raised crucial concerns about their potential to perpetuate biases and unfairness when deployed in social media contexts.
