161 citations · 242 across the 17 of their papers we have counts for
19 papers
ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning
Viet Dac Lai, Nghia Trung Ngo, Amir Pouran Ben Veyseh +4
Over the last few years, large language models (LLMs) have emerged as the most important breakthroughs in natural language processing (NLP) that fundamentally transform research an…
MEE: A Novel Multilingual Event Extraction Dataset
Amir Pouran Ben Veyseh, Javid Ebrahimi, Franck Dernoncourt +1
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. Due t…
Tutorial Recommendation for Livestream Videos using Discourse-Level Consistency and Ontology-Based Filtering
Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen
Streaming videos is one of the methods for creators to share their creative works with their audience. In these videos, the streamer share how they achieve their final objective by…
Improving Keyphrase Extraction with Data Augmentation and Information Filtering
Amir Pouran Ben Veyseh, Nicole Meister, Franck Dernoncourt +1
Keyphrase extraction is one of the essential tasks for document understanding in NLP. While the majority of the prior works are dedicated to the formal setting, e.g., books, news o…
Symlink: A New Dataset for Scientific Symbol-Description Linking
Viet Dac Lai, Amir Pouran Ben Veyseh, Franck Dernoncourt +1
Mathematical symbols and descriptions appear in various forms across document section boundaries without explicit markup. In this paper, we present a new large-scale dataset that e…
MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction
Amir Pouran Ben Veyseh, Nicole Meister, Seunghyun Yoon +3
Acronym extraction is the task of identifying acronyms and their expanded forms in texts that is necessary for various NLP applications. Despite major progress for this task in rec…
SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptions
Viet Dac Lai, Amir Pouran Ben Veyseh, Franck Dernoncourt +1
Given the increasing number of livestreaming videos, automatic speech recognition and post-processing for livestreaming video transcripts are crucial for efficient data management…
Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey
Bonan Min, Hayley Ross, Elior Sulem +6
Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field. We present a survey of recent work that uses…
MadDog: A Web-based System for Acronym Identification and Disambiguation
Amir Pouran Ben Veyseh, Franck Dernoncourt, Walter Chang +1
Acronyms and abbreviations are the short-form of longer phrases and they are ubiquitously employed in various types of writing. Despite their usefulness to save space in writing an…
Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding
Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen +2
Acronyms are the short forms of longer phrases and they are frequently used in writing, especially scholarly writing, to save space and facilitate the communication of information.…
Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing
Minh Van Nguyen, Viet Dac Lai, Amir Pouran Ben Veyseh +1
We introduce Trankit, a light-weight Transformer-based Toolkit for multilingual Natural Language Processing (NLP). It provides a trainable pipeline for fundamental NLP tasks over 1…
What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation
Amir Pouran Ben Veyseh, Franck Dernoncourt, Quan Hung Tran +1
Acronyms are the short forms of phrases that facilitate conveying lengthy sentences in documents and serve as one of the mainstays of writing. Due to their importance, identifying…
Graph Transformer Networks with Syntactic and Semantic Structures for Event Argument Extraction
Amir Pouran Ben Veyseh, Tuan Ngo Nguyen, Thien Huu Nguyen
The goal of Event Argument Extraction (EAE) is to find the role of each entity mention for a given event trigger word. It has been shown in the previous works that the syntactic st…
Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation
Amir Pouran Ben Veyseh, Nasim Nour, Franck Dernoncourt +3
Aspect-based Sentiment Analysis (ABSA) seeks to predict the sentiment polarity of a sentence toward a specific aspect. Recently, it has been shown that dependency trees can be inte…
Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning
Amir Pouran Ben Veyseh, Nasim Nouri, Franck Dernoncourt +2
Targeted opinion word extraction (TOWE) is a sub-task of aspect based sentiment analysis (ABSA) which aims to find the opinion words for a given aspect-term in a sentence. Despite…
Improving Slot Filling by Utilizing Contextual Information
Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen
Slot Filling (SF) is one of the sub-tasks of Spoken Language Understanding (SLU) which aims to extract semantic constituents from a given natural language utterance. It is formulat…
A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency
Amir Pouran Ben Veyseh, Franck Dernoncourt, Dejing Dou +1
Definition Extraction (DE) is one of the well-known topics in Information Extraction that aims to identify terms and their corresponding definitions in unstructured texts. This tas…
Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control
Amir Pouran Ben Veyseh, Thien Huu Nguyen, Dejing Dou
Relation Extraction (RE) is one of the fundamental tasks in Information Extraction and Natural Language Processing. Dependency trees have been shown to be a very useful source of i…
Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures
Amir Pouran Ben Veyseh, Thien Huu Nguyen, Dejing Dou
Event factuality prediction (EFP) is the task of assessing the degree to which an event mentioned in a sentence has happened. For this task, both syntactic and semantic information…