Papers by Xiaoli Fern
Description-Based Zero-shot Fine-Grained Entity Typing (N19-1)
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| Challenge: | Existing systems consider a small set of coarse types, but fine-grained Entity Typing can be used for a variety of tasks. |
| Approach: | They propose a zero-shot entity typing approach that utilizes the type description available from Wikipedia to build a distributed semantic representation of the types. |
| Outcome: | The proposed method is able to recognize novel types without additional training on a public benchmark dataset. |
Text Counterfactuals via Latent Optimization and Shapley-Guided Search (2021.emnlp-main)
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| Challenge: | Using latent optimization and Shapley values, we generate a set of minimal modifications to the text to change the classifier's prediction. |
| Approach: | They propose to generate a counterfactual by making minimal modifications to the text to change the model's prediction. |
| Outcome: | The proposed approach achieves favorable performance compared to white-box and black-box baselines using human and automatic evaluations. |
Saliency Learning: Teaching the Model Where to Pay Attention (N19-1)
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| Challenge: | Recent work on explanation and interpretation has introduced methods to provide insights toward the model’s behaviour and predictions, but they do not improve the model's reliability. |
| Approach: | They propose to provide explanation training and ensure alignment of model’s explanation with ground truth explanation to ensure the model makes correct predictions for the right reason. |
| Outcome: | The proposed method produces more reliable predictions while delivering better results compared to traditional models. |
Dependent Gated Reading for Cloze-Style Question Answering (C18-1)
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| Challenge: | Existing approaches do not fully exploit the interdependency between document and query. |
| Approach: | They propose a novel dependent gated reading bidirectional GRU network to efficiently model the relationship between the document and the query during encoding and decision making. |
| Outcome: | The proposed model performs well on machine comprehension benchmarks such as the Children’s Book Test and Who DiD What. |
Relation Extraction with Explanation (2020.acl-main)
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| Challenge: | Recent studies focus on improving relation extraction accuracy but little is known about their explanability. |
| Approach: | They propose to automatically generate "distractor" sentences to augment the bags and train the model to ignore the distractors. |
| Outcome: | The proposed model improves extraction accuracy while also explanability. |
Joint Neural Entity Disambiguation with Output Space Search (C18-1)
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| Challenge: | Existing models for entity disambiguation combine local contextual information and global evidences. |
| Approach: | They propose a limited discrepancy search model that combines local contextual information and global evidences to improve a local solution from a global view point. |
| Outcome: | The proposed model improves local and global solutions on CoNLL 2003 and TAC 2010 benchmarks. |
Event Detection with Neural Networks: A Rigorous Empirical Evaluation (D18-1)
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| Challenge: | Neural network models have been the most successful for event detection, but they ignore syntactic relationships in the text. |
| Approach: | They propose a GRU-based model that combines syntactic information along with temporal structure through an attention mechanism. |
| Outcome: | The proposed model is competitive with existing models on a ACE2005 dataset. |
Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference (D18-1)
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| Challenge: | In this paper, we examine the behavior of deep learning models in their intermediate layers . saliency determines what is critical for the final decision of a deep model . |
| Approach: | They propose to interpret the intermediate layers of deep models by visualizing the saliency of attention and LSTM gating signals. |
| Outcome: | The proposed methods reveal interesting insights and identify critical information contributing to the model decisions. |
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference (N18-1)
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Reza Ghaeini, Sadid A. Hasan, Vivek Datla, Joey Liu, Kathy Lee, Ashequl Qadir, Yuan Ling, Aaditya Prakash, Xiaoli Fern, Oladimeji Farri
| Challenge: | Existing approaches to natural language inference rely on simple reading mechanisms for independent encoding of the premise and hypothesis. |
| Approach: | They propose a novel bidirectional dependent reading network to efficiently model the relationship between a premise and a hypothesis during encoding and inference. |
| Outcome: | The proposed model outperforms existing methods by a considerable margin on the Stanford Natural Language Inference (SNLI) dataset. |