Search Results for author:
Found 38 papers, 22 papers with code
CATAMARAN: A Cross-lingual Long Text Abstractive Summarization Dataset
Cross-lingual summarization, which produces the summary in one language from a given source document in another language, could be extremely helpful for humans to obtain information across the world.
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
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.
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 .
DLUE: Benchmarking Document Language Understanding
Understanding documents is central to many real-world tasks but remains a challenging topic.
Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models
Memory is one of the most essential cognitive functions serving as a repository of world knowledge and episodes of activities.
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.
A Drop of Ink Makes a Million Think: The Spread of False Information in Large Language Models
Large language models (LLMs) have gained increasing prominence in artificial intelligence, making a profound impact on society and various industries like business and science.
ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models
(4) Can GPTs effectively leverage commonsense for answering questions?
The Life Cycle of Knowledge in Big Language Models: A Survey
Knowledge plays a critical role in artificial intelligence.
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.
Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction
Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching.
Few-shot Named Entity Recognition with Self-describing Networks
In this paper, we propose a self-describing mechanism for few-shot NER, which can effectively leverage illustrative instances and precisely transfer knowledge from external resources by describing both entity types and mentions using a universal concept set.
Can Prompt Probe Pretrained Language Models? Understanding the Invisible Risks from a Causal View
Prompt-based probing has been widely used in evaluating the abilities of pretrained language models (PLMs).
ECO v1: Towards Event-Centric Opinion Mining
Events are considered as the fundamental building blocks of the world.
Pre-training to Match for Unified Low-shot Relation Extraction
Low-shot relation extraction~(RE) aims to recognize novel relations with very few or even no samples, which is critical in real scenario application.
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.
Fine-grained Entity Typing via Label Reasoning
Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types.
Ranked #7 on
Entity Typing
on Open Entity
Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention
In this paper, we identify and solve the trigger curse problem in few-shot event detection (FSED) from a causal view.
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision
In this paper, we propose a new framework for unsupervised MRC.
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
De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention
Distant supervision tackles the data bottleneck in NER by automatically generating training instances via dictionary matching.
Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model
Specifically, during neural network training, we naturally model the noise samples in each batch following a hypergeometric distribution parameterized by the noise-rate.
Element Intervention for Open Relation Extraction
Open relation extraction aims to cluster relation instances referring to the same underlying relation, which is a critical step for general relation extraction.
Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases
Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable knowledge source.
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.
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.
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.
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.
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.
Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension
Then we propose a multi-knowledge reasoning model, which selects inference rules for a specific reasoning context using attention mechanism, and reasons by summarizing all valid inference rules.
