Search Results for author:
Found 32 papers, 7 papers with code
EasyECR: A Library for Easy Implementation and Evaluation of Event Coreference Resolution Models
Event Coreference Resolution (ECR) is the task of clustering event mentions that refer to the same real-world event.
Intrinsic Evaluation of Unlearning Using Parametric Knowledge Traces
To this end, we propose a general methodology for eliciting directions in the parameter space (termed "concept vectors") that encode concrete concepts, and construct ConceptVectors, a benchmark dataset containing hundreds of common concepts and their parametric knowledge traces within two open-source LLMs.
An Empirical Study of Benchmarking Chinese Aspect Sentiment Quad Prediction
Aspect sentiment quad prediction (ASQP) is a critical subtask of aspect-level sentiment analysis.
Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey
This survey presents a comprehensive overview of natural language interfaces for tabular data querying and visualization, which allow users to interact with data using natural language queries.
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching
Subgraph matching is a fundamental building block for graph-based applications and is challenging due to its high-order combinatorial nature.
A Unified One-Step Solution for Aspect Sentiment Quad Prediction
Aspect sentiment quad prediction (ASQP) is a challenging yet significant subtask in aspect-based sentiment analysis as it provides a complete aspect-level sentiment structure.
A Diffusion Model for Event Skeleton Generation
Event skeleton generation, aiming to induce an event schema skeleton graph with abstracted event nodes and their temporal relations from a set of event instance graphs, is a critical step in the temporal complex event schema induction task.
Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks
Neural architecture search (NAS) for Graph neural networks (GNNs), called NAS-GNNs, has achieved significant performance over manually designed GNN architectures.
Towards Effective Collaborative Learning in Long-Tailed Recognition
In this paper, we observe that the knowledge transfer between experts is imbalanced in terms of class distribution, which results in limited performance improvement of the minority classes.
Trigger-free Event Detection via Derangement Reading Comprehension
Event detection (ED), aiming to detect events from texts and categorize them, is vital to understanding actual happenings in real life.
Vision-and-Language Pretrained Models: A Survey
Pretrained models have produced great success in both Computer Vision (CV) and Natural Language Processing (NLP).
Sequential Attention Module for Natural Language Processing
In this paper, we target at how to further improve the token representations on the language models.
RefBERT: Compressing BERT by Referencing to Pre-computed Representations
Differently, in this paper, we propose RefBERT to leverage the knowledge learned from the teacher, i. e., facilitating the pre-computed BERT representation on the reference sample and compressing BERT into a smaller student model.
MagicPai at SemEval-2021 Task 7: Method for Detecting and Rating Humor Based on Multi-Task Adversarial Training
This paper describes MagicPai's system for SemEval 2021 Task 7, HaHackathon: Detecting and Rating Humor and Offense.
PALI at SemEval-2021 Task 2: Fine-Tune XLM-RoBERTa for Word in Context Disambiguation
Second, we construct a new vector on the fine-tuned embeddings from XLM-RoBERTa and feed it to a fully-connected network to output the probability of whether the target word in the context has the same meaning or not.
Sattiy at SemEval-2021 Task 9: An Ensemble Solution for Statement Verification and Evidence Finding with Tables
Question answering from semi-structured tables can be seen as a semantic parsing task and is significant and practical for pushing the boundary of natural language understanding.
Emotion Dynamics Modeling via BERT
Emotion dynamics modeling is a significant task in emotion recognition in conversation.
Automatic Intent-Slot Induction for Dialogue Systems
Automatically and accurately identifying user intents and filling the associated slots from their spoken language are critical to the success of dialogue systems.
KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph
Entity synonyms discovery is crucial for entity-leveraging applications.
FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection
This paper describes the model we apply in the SemEval-2020 Task 10.
UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction
The goal of this task is to extract definition, word level BIO tags and relations.
Making Online Sketching Hashing Even Faster
Data-dependent hashing methods have demonstrated good performance in various machine learning applications to learn a low-dimensional representation from the original data.
Effective Data-aware Covariance Estimator from Compressed Data
Estimating covariance matrix from massive high-dimensional and distributed data is significant for various real-world applications.
Block-term Tensor Neural Networks
Deep neural networks (DNNs) have achieved outstanding performance in a wide range of applications, e. g., image classification, natural language processing, etc.
Hierarchical Context Enhanced Multi-Domain Dialogue System for Multi-domain Task Completion
Task 1 of the DSTC8-track1 challenge aims to develop an end-to-end multi-domain dialogue system to accomplish complex users' goals under tourist information desk settings.
BERT Meets Chinese Word Segmentation
Chinese word segmentation (CWS) is a fundamental task for Chinese language understanding.
HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition
In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: (1) the same word can deliver different emotions in different contexts; (2) some emotions are rarely seen in general dialogues; (3) long-range contextual information is hard to be effectively captured.
EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogues
The BiLSTM exhibits the power of modeling the word dependencies, and extracting the most relevant features for emotion classification.
BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition
Recently, deep neural networks (DNNs) have been regarded as the state-of-the-art classification methods in a wide range of applications, especially in image classification.
A deep learning approach for predicting the quality of online health expert question-answering services
Finally, a deep belief network (DBN)-based HQA answer quality prediction framework is proposed to predict the quality of answers by learning the high-level hidden semantic representation from the physicians' answers.
Efficient Non-oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee
Previously, oblivious random projection based approaches that project high dimensional features onto a random subspace have been used in practice for tackling high-dimensionality challenge in machine learning.
