Papers with Deep neural models
PuzzLing Machines: A Challenge on Learning From Small Data (2020.acl-main)
Copied to clipboard
| Challenge: | a benchmark dataset of 81 languages is released to test deep neural models' human-like reasoning and generalization skills. |
| Approach: | They propose a challenge on learning from small data using Rosetta Stone puzzles from Linguistic Olympiads for high school students. |
| Outcome: | The proposed benchmark consists of Rosetta Stone puzzles from Linguistic Olympiads for high school students. |
Reducing Spurious Correlations for Answer Selection by Feature Decorrelation and Language Debiasing (2022.coling-1)
Copied to clipboard
| Challenge: | Existing deep neural models rely on spurious correlations between prediction labels and input features, which in general suffer from robustness and generalization. |
| Approach: | They propose a feature decorrelation module to remove feature dependencies and reduce spurious correlations by learning a weight for each instance at the training phase. |
| Outcome: | The proposed method improves the robustness of the neural ANswer selection models from the sample and feature perspectives. |
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction (2023.eacl-main)
Copied to clipboard
Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar, Bill Yuchen Lin, Fred Morstatter, Jay Pujara, Elizabeth Boschee, James Allan, Xiang Ren
| Challenge: | Named entity recognition models have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations. |
| Approach: | They propose a framework that automatically generates and leverages “entity triggers” which are human-readable cues in the text that help guide the model to make better decisions. |
| Outcome: | The proposed framework outperforms the RoBERTa-CRF baseline by nearly 0.5 F1 points on three well-studied datasets. |
Human or Neural Translation? (2020.coling-main)
Copied to clipboard
Shivendra Bhardwaj, David Alfonso Hermelo, Phillippe Langlais, Gabriel Bernier-Colborne, Cyril Goutte, Michel Simard
| Challenge: | a recent study shows that deep neural models have improved machine translation . identifying machine translation is still feasible, but is not yet known. |
| Approach: | They train and apply deep neural models to distinguish between human and machine translations . they use a monolingual and bilingual task to train and train 18 classifiers based on their results . |
| Outcome: | The proposed model improves the ability to distinguish between human and machine translations at the sentence level. |