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Found 38 papers, 8 papers with code
Event Detection for Suicide Understanding
In particular, we introduce SuicideED: a new dataset for the ED task that features seven suicidal event types to comprehensively capture suicide actions and ideation, and general risk and protective factors.
BehancePR: A Punctuation Restoration Dataset for Livestreaming Video Transcript
This work presents a new human-annotated corpus, called BehancePR, for punctuation restoration in livestreaming video transcripts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
SemEval 2022 Task 12: Symlink - Linking Mathematical Symbols to their Descriptions
We describe Symlink, a SemEval shared task of extracting mathematical symbols and their descriptions from LaTeX source of scientific documents.
Transfer Learning and Prediction Consistency for Detecting Offensive Spans of Text
Toxic span detection is the task of recognizing offensive spans in a text snippet.
Event Extraction in Video Transcripts
As such, the challenges of EE in informal and noisy texts are not adequately studied.
Keyphrase Prediction from Video Transcripts: New Dataset and Directions
To this end, this work studies new challenges of KP in transcripts of videos, an understudied domain for KP that involves informal texts and non-cohesive presentation styles.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Word-Label Alignment for Event Detection: A New Perspective via Optimal Transport
Event Detection (ED) aims to identify mentions/triggers of real world events in text.
Modeling Document-Level Context for Event Detection via Important Context Selection
To address this issue, we propose a novel method to model document-level context for ED that dynamically selects relevant sentences in the document for the event prediction of the target sentence.
Document-Level Event Argument Extraction via Optimal Transport
Event Argument Extraction (EAE) is one of the sub-tasks of event extraction, aiming to recognize the role of each entity mention toward a specific event trigger.
BehanceCC: A ChitChat Detection Dataset For Livestreaming Video Transcripts
Livestreaming videos have become an effective broadcasting method for both video sharing and educational purposes.
MECI: A Multilingual Dataset for Event Causality Identification
Our dataset thus enable a new research direction on cross-lingual transfer learning for ECI.
Introducing a New Dataset for Event Detection in Cybersecurity Texts
Detecting cybersecurity events is necessary to keep us informed about the fast growing number of such events reported in text.
BehanceQA: A New Dataset for Identifying Question-Answer Pairs in Video Transcripts
Question-Answer (QA) is one of the effective methods for storing knowledge which can be used for future retrieval.
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.
MEE: A Novel Multilingual Event Extraction Dataset
Event Extraction (EE) is one of the fundamental tasks in Information Extraction (IE) that aims to recognize event mentions and their arguments (i. e., participants) from text.
MINION: a Large-Scale and Diverse Dataset for Multilingual Event Detection
Event Detection (ED) is the task of identifying and classifying trigger words of event mentions in text.
Tutorial Recommendation for Livestream Videos using Discourse-Level Consistency and Ontology-Based Filtering
In order to alleviate this issue, one solution is to link the streaming videos with the relevant tutorial available for the tools used in the streaming video.
Improving Keyphrase Extraction with Data Augmentation and Information Filtering
Keyphrase extraction is one of the essential tasks for document understanding in NLP.
Symlink: A New Dataset for Scientific Symbol-Description Linking
Mathematical symbols and descriptions appear in various forms across document section boundaries without explicit markup.
MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction
Acronym extraction is the task of identifying acronyms and their expanded forms in texts that is necessary for various NLP applications.
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
Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey
Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field.
Unleash GPT-2 Power for Event Detection
To prevent the noises inevitable in automatically generated data from hampering training process, we propose to exploit a teacher-student architecture in which the teacher is supposed to learn anchor knowledge from the original data.
DPR at SemEval-2021 Task 8: Dynamic Path Reasoning for Measurement Relation Extraction
To this end, in this paper, we propose a novel model for the task of measurement relation extraction (MRE) whose goal is to recognize the relation between measured entities, quantities, and conditions mentioned in a document.
MadDog: A Web-based System for Acronym Identification and Disambiguation
However, none of the existing works provide a unified solution capable of processing acronyms in various domains and to be publicly available.
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
Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding
To push forward research in this direction, we have organized two shared task for acronym identification and acronym disambiguation in scientific documents, named AI@SDU and AD@SDU, respectively.
What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation
The proposed model outperforms the state-of-the-art models on the new AD dataset, providing a strong baseline for future research on this dataset.
Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation
In addition, we propose a mechanism to obtain the importance scores for each word in the sentences based on the dependency trees that are then injected into the model to improve the representation vectors for ABSA.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Graph Transformer Networks with Syntactic and Semantic Structures for Event Argument Extraction
The goal of Event Argument Extraction (EAE) is to find the role of each entity mention for a given event trigger word.
Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning
In this work, we propose to incorporate the syntactic structures of the sentences into the deep learning models for TOWE, leveraging the syntax-based opinion possibility scores and the syntactic connections between the words.
Aspect-Based Sentiment Analysis
Aspect-oriented Opinion Extraction
+1
Exploiting the Syntax-Model Consistency for Neural Relation Extraction
In order to overcome these issues, we propose a novel deep learning model for RE that uses the dependency trees to extract the syntax-based importance scores for the words, serving as a tree representation to introduce syntactic information into the models with greater generalization.
Improving Slot Filling by Utilizing Contextual Information
To address this issue, in this paper, we propose a novel method to incorporate the contextual information in two different levels, i. e., representation level and task-specific (i. e., label) level.
Ranked #5 on
Intent Detection
on SNIPS
A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency
In this work, we propose a novel model for DE that simultaneously performs the two tasks in a single framework to benefit from their inter-dependencies.
Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control
The current deep learning models for relation extraction has mainly exploited this dependency information by guiding their computation along the structures of the dependency trees.
Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures
In this work, we introduce a novel graph-based neural network for EFP that can integrate the semantic and syntactic information more effectively.
