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Found 32 papers, 17 papers with code
ISCAS at SemEval-2022 Task 10: An Extraction-Validation Pipeline for Structured Sentiment Analysis
ISCAS participated in both sub-tasks in SemEval-2022 Task 10: Structured Sentiment competition.
URL: Universal Referential Knowledge Linking via Task-instructed Representation Compression
Linking a claim to grounded references is a critical ability to fulfill human demands for authentic and reliable information.
Few-shot Named Entity Recognition via Superposition Concept Discrimination
Then a superposition instance retriever is applied to retrieve corresponding instances of these superposition concepts from large-scale text corpus.
Meta-Cognitive Analysis: Evaluating Declarative and Procedural Knowledge in Datasets and Large Language Models
Declarative knowledge and procedural knowledge are two key parts in meta-cognitive theory, and these two hold significant importance in pre-training and inference of LLMs.
ShortGPT: Layers in Large Language Models are More Redundant Than You Expect
As Large Language Models (LLMs) continue to advance in performance, their size has escalated significantly, with current LLMs containing billions or even trillions of parameters.
SoFA: Shielded On-the-fly Alignment via Priority Rule Following
The alignment problem in Large Language Models (LLMs) involves adapting them to the broad spectrum of human values.
Executing Natural Language-Described Algorithms with Large Language Models: An Investigation
In this paper, we seek to examine the capacity of present-day LLMs to comprehend and execute algorithms outlined in natural language.
Self-Retrieval: Building an Information Retrieval System with One Large Language Model
The rise of large language models (LLMs) has transformed the role of information retrieval (IR) systems in the way to humans accessing information.
Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models?
Building machines with commonsense has been a longstanding challenge in NLP due to the reporting bias of commonsense rules and the exposure bias of rule-based commonsense reasoning.
Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting
Incorporating factual knowledge in knowledge graph is regarded as a promising approach for mitigating the hallucination of large language models (LLMs).
Learning In-context Learning for Named Entity Recognition
M}$, and a new entity extractor can be implicitly constructed by applying new instruction and demonstrations to PLMs, i. e., $\mathcal{ (\lambda .
Harvesting Event Schemas from Large Language Models
Event schema provides a conceptual, structural and formal language to represent events and model the world event knowledge.
Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns
LLMs, as AI agents, can observe external information, which shapes their cognition and behaviors.
ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models
(4) Can ChatGPT effectively leverage commonsense for answering questions?
Universal Information Extraction as Unified Semantic Matching
Based on this paradigm, we propose to universally model various IE tasks with Unified Semantic Matching (USM) framework, which introduces three unified token linking operations to model the abilities of structuring and conceptualizing.
Unified Structure Generation for Universal Information Extraction
Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas.
Ranked #4 on
Aspect-Based Sentiment Analysis (ABSA)
on ASTE
(using extra training data)
Procedural Text Understanding via Scene-Wise Evolution
In this paper, we propose a new \textbf{scene-wise} paradigm for procedural text understanding, which jointly tracks states of all entities in a scene-by-scene manner.
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction
Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event.
Ranked #3 on
Event Extraction
on ACE2005
From Discourse to Narrative: Knowledge Projection for Event Relation Extraction
Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events.
Syntactic and Semantic-driven Learning for Open Information Extraction
One of the biggest bottlenecks in building accurate, high coverage neural open IE systems is the need for large labelled corpora.
ISCAS at SemEval-2020 Task 5: Pre-trained Transformers for Counterfactual Statement Modeling
ISCAS participated in two subtasks of SemEval 2020 Task 5: detecting counterfactual statements and detecting antecedent and consequence.
End-to-End Neural Event Coreference Resolution
Traditional event coreference systems usually rely on pipeline framework and hand-crafted features, which often face error propagation problem and have poor generalization ability.
A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land?
Specifically, we erase name regularity, mention coverage and context diversity respectively from the benchmarks, in order to explore their impact on the generalization ability of models.
Iterative Dual Domain Adaptation for Neural Machine Translation
Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model.
Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition
Current region-based NER models only rely on fully-annotated training data to learn effective region encoder, which often face the training data bottleneck.
Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning
Event detection systems rely on discrimination knowledge to distinguish ambiguous trigger words and generalization knowledge to detect unseen/sparse trigger words.
Cost-sensitive Regularization for Label Confusion-aware Event Detection
In supervised event detection, most of the mislabeling occurs between a small number of confusing type pairs, including trigger-NIL pairs and sibling sub-types of the same coarse type.
Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks
In this paper, we propose to resolve this problem by modeling and leveraging the head-driven phrase structures of entity mentions, i. e., although a mention can nest other mentions, they will not share the same head word.
Ranked #7 on
Nested Mention Recognition
on ACE 2005
Adaptive Scaling for Sparse Detection in Information Extraction
This paper focuses on detection tasks in information extraction, where positive instances are sparsely distributed and models are usually evaluated using F-measure on positive classes.
Nugget Proposal Networks for Chinese Event Detection
Neural network based models commonly regard event detection as a word-wise classification task, which suffer from the mismatch problem between words and event triggers, especially in languages without natural word delimiters such as Chinese.
Variational Recurrent Neural Machine Translation
Partially inspired by successful applications of variational recurrent neural networks, we propose a novel variational recurrent neural machine translation (VRNMT) model in this paper.
