Search Results for author:
Found 17 papers, 4 papers with code
Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks
Recent studies on event detection (ED) have shown that the syntactic dependency graph can be employed in graph convolution neural networks (GCN) to achieve state-of-the-art performance.
Event Extraction in Video Transcripts
As such, the challenges of EE in informal and noisy texts are not adequately studied.
MECI: A Multilingual Dataset for Event Causality Identification
Our dataset thus enable a new research direction on cross-lingual transfer learning for ECI.
DocFinQA: A Long-Context Financial Reasoning Dataset
For large language models (LLMs) to be effective in the financial domain -- where each decision can have a significant impact -- it is necessary to investigate realistic tasks and data.
CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages
However, when it comes to training datasets for these LLMs, especially the recent state-of-the-art models, they are often not fully disclosed.
Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback
Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research.
Boosting Punctuation Restoration with Data Generation and Reinforcement Learning
Punctuation restoration is an important task in automatic speech recognition (ASR) which aim to restore the syntactic structure of generated ASR texts to improve readability.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning
The answer to this question requires a thorough evaluation of ChatGPT over multiple tasks with diverse languages and large datasets (i. e., beyond reported anecdotes), which is still missing or limited in current research.
Event Extraction: A Survey
In this report, we provide the task definition, the evaluation method, as well as the benchmark datasets and a taxonomy of methodologies for event extraction.
Symlink: A New Dataset for Scientific Symbol-Description Linking
Mathematical symbols and descriptions appear in various forms across document section boundaries without explicit markup.
SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptions
Given the increasing number of livestreaming videos, automatic speech recognition and post-processing for livestreaming video transcripts are crucial for efficient data management as well as knowledge mining.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks
Existing works on information extraction (IE) have mainly solved the four main tasks separately (entity mention recognition, relation extraction, event trigger detection, and argument extraction), thus failing to benefit from inter-dependencies between tasks.
Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing
Finally, we create a demo video for Trankit at: https://youtu. be/q0KGP3zGjGc.
Ranked #1 on
Sentence segmentation
on UD2.5 test
Event Detection: Gate Diversity and Syntactic Importance Scoresfor Graph Convolution Neural Networks
Recent studies on event detection (ED) haveshown that the syntactic dependency graph canbe employed in graph convolution neural net-works (GCN) to achieve state-of-the-art per-formance.
Extensively Matching for Few-shot Learning Event Detection
In this work, weformulate event detection as a few-shot learn-ing problem to enable to extend event detec-tion to new event types.
Exploiting the Matching Information in the Support Set for Few Shot Event Classification
The existing event classification (EC) work primarily focuseson the traditional supervised learning setting in which models are unableto extract event mentions of new/unseen event types.
Extending Event Detection to New Types with Learning from Keywords
We introduce a novel feature-based attention mechanism for convolutional neural networks for event detection in the new formulation.
