Papers by Amir Pouran Ben Veyseh
Graph Transformer Networks with Syntactic and Semantic Structures for Event Argument Extraction (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models for Event Argument Extraction fail to exploit semantic structures of sentences to induce effective representations for EAE. |
| Approach: | They propose a novel model that exploits syntactic and semantic structures of sentences to learn more effective sentence structures for EAE. |
| Outcome: | The proposed model improves the performance of the existing models on standard datasets. |
Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing (2021.eacl-demos)
Copied to clipboard
| Challenge: | Trankit is a lightweight, pre-trained toolkit for multilingual natural language processing. |
| Approach: | They propose a transformer-based toolkit for multilingual natural language processing that trains pipelines over 100 languages and 90 pretrained pipelines for 56 languages. |
| Outcome: | The proposed tool outperforms existing pipelines over sentence segmentation, part-of-speech tagging, morphological feature tabbing, and dependency parsing while maintaining competitive performance over tokenization, multi-word token expansion, and lemmatization over 90 Universal Dependencies treebanks. |
BehancePR: A Punctuation Restoration Dataset for Livestreaming Video Transcript (2022.findings-naacl)
Copied to clipboard
| Challenge: | a growing number of livestreaming videos provide useful knowledge with exceptional visual demonstrations. |
| Approach: | They propose a human-annotated corpus for punctuation restoration in livestreaming video transcripts . they show popular natural language processing tools underperform on sentence boundary detection . |
| Outcome: | The proposed dataset shows that natural language processing tools underperform on sentence boundary detection on livestreaming video transcripts. |
Document-Level Event Argument Extraction via Optimal Transport (2022.findings-acl)
Copied to clipboard
| Challenge: | Prior work on event-level EAE models ignore syntactic structures for documents . prior work on EE is restricted to sentence-level setting where event triggers and arguments are assumed to appear in the same sentences. |
| Approach: | They propose to employ Optimal Transport to induce structures of documents based on sentence-level syntactic structures and tailored to EAE task. |
| Outcome: | The proposed model is effective in document-level EAE, with a new constraint on unrelated context words. |
Unleash GPT-2 Power for Event Detection (2021.acl-long)
Copied to clipboard
| Challenge: | Event Detection (ED) aims to recognize mentions of events and their types in text. |
| Approach: | They propose to exploit a pre-trained language model to generate training samples for ED. |
| Outcome: | The proposed model improves on multiple ED benchmark datasets and establishes state-of-the-art results. |
What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation (2020.coling-main)
Copied to clipboard
| Challenge: | Acronyms are short forms of phrases that facilitate conveying lengthy sentences in documents. |
| Approach: | They propose to annotate a large dataset for scientific domain and a new deep learning model which expands an ambiguous acronym in a sentence. |
| Outcome: | The proposed model outperforms the state-of-the-art models on the new dataset. |
Transfer Learning and Prediction Consistency for Detecting Offensive Spans of Text (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing models for toxic span detection only classify text snippets as offensive or not . a novel model seeks to simultaneously predict offensive words and opinion phrases . |
| Approach: | They propose a novel model that seeks to predict offensive words and opinion phrases simultaneously . they also introduce a regularization mechanism to encourage consistency of the model predictions . |
| Outcome: | The proposed model performs well compared to baselines on toxic span detection tasks . it predicts offensive words and opinion phrases to leverage inter-dependencies . |
MadDog: A Web-based System for Acronym Identification and Disambiguation (2021.eacl-demos)
Copied to clipboard
| Challenge: | Acronyms and abbreviations are the short-form of longer phrases and are frequently used in writing but they can also present challenges for newcomers. |
| Approach: | They propose to develop a web-based acronym identification and disambiguation system which can process acronyms from various domains including scientific, biomedical, and general domains. |
| Outcome: | The proposed system can process acronyms from scientific, biomedical, and general domains. |
Word-Label Alignment for Event Detection: A New Perspective via Optimal Transport (2022.starsem-1)
Copied to clipboard
| Challenge: | Event Detection (ED) is a critical task in Information Extraction. |
| Approach: | They propose a word-label alignment task for event detecting . they propose to incorporate word-labeled alignment biases into the equation . |
| Outcome: | The proposed model facilitates incorporation of word-label alignment biases on a benchmark dataset to demonstrate its effectiveness. |
MINION: a Large-Scale and Diverse Dataset for Multilingual Event Detection (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing methods for ED in IE and NLP focus on feature-based models to feature-driven models. |
| Approach: | They propose to use a multilingual dataset to annotate events for 8 different languages . they demonstrate the challenges and transferability of ED across languages in MINION . |
| Outcome: | a new dataset that consistently annotates events for 8 different languages is released . the new dataset will promote future research on multilingual ED . |
Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures (P19-1)
Copied to clipboard
| Challenge: | Existing work on event factuality prediction (EFP) relies on syntactic and semantic information to identify important context words. |
| Approach: | They propose a graph-based neural network that integrates syntactic and semantic information more effectively. |
| Outcome: | The proposed model integrates syntactic and semantic information more effectively . it provides more meaningful information for downstream tasks than classification formulations . |
MEE: A Novel Multilingual Event Extraction Dataset (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for Event Extraction are limited for non-English languages . lack of high-quality multilingual datasets has been the main hindrance . |
| Approach: | They propose a multilingual event extraction dataset that provides annotation for more than 50K event mentions in 8 typologically different languages. |
| Outcome: | The proposed dataset provides annotation for more than 50K event mentions in 8 languages . the proposed dataset will be publicly available to foster future research . |
BehanceCC: A ChitChat Detection Dataset For Livestreaming Video Transcripts (2022.lrec-1)
Copied to clipboard
| Challenge: | livestreaming videos contain a considerable amount of off-topic content, causing noises and data load to downstream applications. |
| Approach: | They propose a human-annotated benchmark dataset for off-topic detection in livestreaming video transcripts. |
| Outcome: | The proposed dataset reveals the complexity of chitchat detection in livestreaming videos . livestreams tend to be longer than pre-recorded videos and have fewer verbal pauses . |
MCECR: A Novel Dataset for Multilingual Cross-Document Event Coreference Resolution (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing datasets for event coreference resolution focus on within-document event coreference and English text, lacking cross-document ECR datasets beyond English. |
| Approach: | They propose a multiligual dataset that manually annotates documents for event mentions and coreference in five languages. |
| Outcome: | The proposed dataset annotates documents for event mentions and coreference in five languages . the dataset fetches related news articles from the google search engine to increase the number of non-singleton clusters . |
Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning (2020.emnlp-main)
Copied to clipboard
| Challenge: | Current deep learning models fail to exploit syntactic information of sentences . proposed model incorporates syntax-based opinion possibility scores and syntaktic connections between the words . |
| Approach: | They propose to incorporate syntactic information of sentences into deep learning models for TOWE . they propose a novel regularization technique to improve the performance of the models . |
| Outcome: | The proposed model achieves state-of-the-art on four benchmark datasets. |
ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in natural language processing (NLP) have led to significant breakthroughs in the field. |
| Approach: | They evaluate ChatGPT over multiple tasks with diverse languages and large datasets to provide more comprehensive information for multilingual NLP applications. |
| Outcome: | The proposed model can process and generate texts for multiple languages due to its multilingual training data. |
Event Detection for Suicide Understanding (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for detecting suicide-related events are limited . recognizing suicide- related events is critical to understanding the condition, authors argue . |
| Approach: | They propose a dataset to detect event trigger words of suicide-related events in forums . they propose 'suicideED' dataset to capture suicidal actions and ideation . |
| Outcome: | The proposed dataset captures suicide actions and ideation, and general risk and protective factors. |
Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation (2020.findings-emnlp)
Copied to clipboard
Amir Pouran Ben Veyseh, Nasim Nouri, Franck Dernoncourt, Quan Hung Tran, Dejing Dou, Thien Huu Nguyen
| Challenge: | Aspect-based Sentiment Analysis (ABSA) seeks to predict sentiment polarity of input sentences toward a specific aspect. |
| Approach: | They propose a graph-based deep learning model that integrates dependency trees into deep learning models to improve ABSA performance. |
| Outcome: | The proposed model achieves state-of-the-art on three benchmark datasets. |
BehanceQA: A New Dataset for Identifying Question-Answer Pairs in Video Transcripts (2022.lrec-1)
Copied to clipboard
| Challenge: | Question-Answer (QA) is an effective method for storing knowledge . prior QA identification systems have been limited to formal written documents . a large-scale QA dataset annotated by human over 500 hours of video transcripts is a challenge . |
| Approach: | They present a large-scale QA identification dataset annotated by human over 500 hours of video transcripts. |
| Outcome: | The proposed dataset presents unique challenges for existing methods . it shows that the annotated dataset presents challenges for new methods - the results will be released . |
Modeling Document-Level Context for Event Detection via Important Context Selection (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for Event Detection (ED) do not encode long-range document-level context . e.g., BERT cannot encode long text-level contextual information . |
| Approach: | They propose a method to model document-level context for Event Detection using transformer-based language models. |
| Outcome: | The proposed model can predict event prediction of target sentence in document-level context . the proposed model is effective on multiple benchmark datasets . |
Introducing a New Dataset for Event Detection in Cybersecurity Texts (2020.emnlp-main)
Copied to clipboard
| Challenge: | a large amount of text data is produced to report and discuss cyber vulnerabilities . detecting cybersecurity events is necessary to keep us informed about the fast growing number of such events reported in text. |
| Approach: | They propose a dataset characterizing the manual annotation for 30 important cybersecurity event types and a large dataset to develop deep learning models. |
| Outcome: | The proposed dataset characterizes the manual annotation for 30 important event types and supports the modeling of document-level information to improve the performance. |
Exploiting the Syntax-Model Consistency for Neural Relation Extraction (2020.acl-main)
Copied to clipboard
| Challenge: | Existing deep learning models for Relation Extraction (RE) have limited generalization beyond the syntactic structures of the input sentences. |
| Approach: | They propose a deep learning model that uses dependency trees to extract syntactic importance of words for Relation Extraction. |
| Outcome: | The proposed model outperforms existing models on three RE benchmark datasets. |
Generating Labeled Data for Relation Extraction: A Meta Learning Approach with Joint GPT-2 Training (2023.findings-acl)
Copied to clipboard
| Challenge: | Relation Extraction (RE) is the task of identifying semantic relation between entities mentioned in text. |
| Approach: | They propose a framework to automatically generate labeled data for Relation Extraction . they propose 'reward function' to update pre-trained language model for RE . |
| Outcome: | The proposed framework generates labeled data for relation extraction using a pre-trained language model and a meta learning approach to improve the generated samples. |