Papers with integer linear programming
Robustness Evaluation of Text Classification Models Using Mathematical Optimization and Its Application to Adversarial Training (2022.findings-aacl)
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| Challenge: | Neural networks are vulnerable to adversarial examples due to slightly perturbed input data. |
| Approach: | They propose a method that evaluates the robustness of text classification models by an optimization problem that identifies a minimum synonym swap that changes the classification result. |
| Outcome: | The proposed method achieves high scores in human evaluations of grammatical correctness and semantic similarity for an IMDb dataset and implements adversarial training with the IMD and SST2 datasets. |
Joint Reasoning for Temporal and Causal Relations (P18-1)
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| Challenge: | a cause must occur earlier than its effect, temporal and causal relations are closely related . a joint inference framework is developed for studying temporal, causal relations . |
| Approach: | They propose a joint inference framework for temporal and causal relations . they use constraints inherent in time and causality to enforce constraints . |
| Outcome: | The proposed framework improves extraction of temporal and causal relations from text. |
Easy First Relation Extraction with Information Redundancy (D19-1)
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| Challenge: | Existing relation extraction models make decisions globally using integer linear programming . Existing approaches require time and memory to encode redundant information for ILP . |
| Approach: | They propose an easy first approach for relation extraction with information redundancies embedded in local sentence extractors to resolve conflict decisions with domain and uniqueness constraints. |
| Outcome: | The proposed approach outperforms both ILP and neural network-based methods in relation extraction (RE) studies have shown that the proposed approach improves the efficiency and accuracy of RE models. |
SpanAlign: Sentence Alignment Method based on Cross-Language Span Prediction and ILP (2020.coling-main)
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| Challenge: | Existing methods for automatic sentence alignment assume monotonic alignments, but they can handle non-monotonic alignments. |
| Approach: | They propose a method to automatically extract parallel sentences from noisy parallel documents by embeddings and encoding each source and target sentence. |
| Outcome: | The proposed method improves translation accuracy by 4.1 BLEU scores on English-Japanese . it can predict spans in target document from sentences in source document . |
An Improved Neural Baseline for Temporal Relation Extraction (D19-1)
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| Challenge: | Existing datasets are small and/or have low inter-annotator agreements. |
| Approach: | They propose a new neural system that achieves 10% absolute accuracy improvement over the previous best system. |
| Outcome: | The proposed system achieves 10% absolute improvement over the previous best system on two benchmark datasets. |
Goal-Driven Explainable Clustering via Language Descriptions (2023.emnlp-main)
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| Challenge: | Existing formulations neither consider the users’ goals nor explain clusters’ meanings. |
| Approach: | They propose a task formulation that represents both the goal and the explanations as free-form language descriptions. |
| Outcome: | The proposed method produces more accurate and goal-related explanations than previous methods. |