Papers by Yaser Al-Onaizan
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events (2020.emnlp-main)
Copied to clipboard
Miguel Ballesteros, Rishita Anubhai, Shuai Wang, Nima Pourdamghani, Yogarshi Vyas, Jie Ma, Parminder Bhatia, Kathleen McKeown, Yaser Al-Onaizan
| Challenge: | Existing models for temporal ordering of events rely on pretrained representations, transfer and multitask learning, and self-training techniques. |
| Approach: | They propose a neural architecture and a set of training methods for ordering events by predicting temporal relations by pre-training models. |
| Outcome: | The proposed models can predict temporal relations between two pairs of events within a span of text and identify temporal relationships between them. |
Label Semantics for Few Shot Named Entity Recognition (2022.findings-acl)
Copied to clipboard
Jie Ma, Miguel Ballesteros, Srikanth Doss, Rishita Anubhai, Sunil Mallya, Yaser Al-Onaizan, Dan Roth
| Challenge: | Named entity recognition (NER) is a fundamental natural language understanding task that requires large amounts of high quality annotated in-domain data. |
| Approach: | They propose a neural architecture that leverages the semantic information in the names of the labels to give the model additional signal and enriched priors. |
| Outcome: | The proposed model is especially effective in low resource settings. |
Resource-Enhanced Neural Model for Event Argument Extraction (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work on event argument extraction (EE) is limited due to data scarcity and lack of a model encoder. |
| Approach: | They propose to capture the long-range dependency between an event trigger and a distant event argument using unlabeled data. |
| Outcome: | Experiments on the English ACE 2005 benchmark show that the proposed method achieves a new state-of-the-art. |
Span-Level Model for Relation Extraction (P19-1)
Copied to clipboard
| Challenge: | Recent approaches for this span-level task have inherent limitations. |
| Approach: | They propose a model which directly models all possible spans and performs joint entity mention detection and relation extraction. |
| Outcome: | The proposed model performs joint entity mention detection and relation extraction on the ACE2005 dataset. |
Training Neural Machine Translation to Apply Terminology Constraints (P19-1)
Copied to clipboard
| Challenge: | Existing methods to integrate domain terminology into neural machine translation (NMT) are brittle when tested in real-world situations. |
| Approach: | They propose a method to inject custom terminology into neural machine translation at run time by using the target side of terminology entries whose source side match the input as decoding-time constraints. |
| Outcome: | The proposed method is faster than state-of-the-art decoding and more efficient than constraint-free decoding. |
Exploring Content Selection in Summarization of Novel Chapters (2020.acl-main)
Copied to clipboard
| Challenge: | We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summary summaries. |
| Approach: | They propose a new metric for aligning summary sentences with chapter sentences to create gold extracts. |
| Outcome: | The proposed method improves on previous methods and automatic metrics and a crowd-sourced pyramid analysis. |
Words Aren’t Enough, Their Order Matters: On the Robustness of Grounding Visual Referring Expressions (2020.acl-main)
Copied to clipboard
| Challenge: | Visual referring expression recognition is a task that requires natural language understanding in the context of an image. |
| Approach: | They propose to use contrastive learning and multi-task learning to increase the robustness of ViLBERT, the current state-of-the-art model for this task. |
| Outcome: | The proposed methods are 12% to 23% lower in performance than the established progress for this task. |
Robustness to Capitalization Errors in Named Entity Recognition (D19-55)
Copied to clipboard
| Challenge: | Existing methods to improve robustness to noise discard given orthographic information, which significantly degrades models' performance on well-formed text. |
| Approach: | They propose a method which allows models to learn to utilize or ignore orthographic information depending on its usefulness in the context. |
| Outcome: | The proposed approach achieves competitive robustness to capitalization errors while making negligible compromises on well-formed text and significantly improving generalization power on noisy user-generated text. |
To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging (2020.emnlp-main)
Copied to clipboard
Kasturi Bhattacharjee, Miguel Ballesteros, Rishita Anubhai, Smaranda Muresan, Jie Ma, Faisal Ladhak, Yaser Al-Onaizan
| Challenge: | Using large amounts of unlabeled data to improve performance has become the foundation for many natural language processing tasks. |
| Approach: | They propose a task-specific semi-supervised approach that uses unlabeled data in a more task-agnostic manner. |
| Outcome: | The proposed approach achieves similar performance to BERT on a set of sequence tagging tasks with less financial and environmental impact. |
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction (2021.findings-acl)
Copied to clipboard
Elsbeth Turcan, Shuai Wang, Rishita Anubhai, Kasturi Bhattacharjee, Yaser Al-Onaizan, Smaranda Muresan
| Challenge: | Detecting what emotions are expressed in text is a well-studied problem in natural language processing. |
| Approach: | They propose methods that combine common-sense knowledge with multi-task learning to perform joint emotion classification and emotion cause tagging. |
| Outcome: | The proposed models improve on both tasks when using common-sense reasoning and a multitask framework. |
Evaluating Robustness to Input Perturbations for Neural Machine Translation (2020.acl-main)
Copied to clipboard
| Challenge: | Recent work has shown that Neural Machine Translation models are brittle to small perturbations in the input. |
| Approach: | They propose to use subword regularization to measure the relative degradation and changes in translation when perturbations are added to the input. |
| Outcome: | The proposed measures show that the models are more robust to perturbations when subword regularization methods are used. |