Papers with augmentation approach
Grounding the Lexical Substitution Task in Entailment (2023.findings-acl)
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| Challenge: | Existing definitions of lexical substitutes are vague or inconsistent with the gold annotations. |
| Approach: | They propose a new definition which is grounded in the relation of entailment . they empirically validate the definition and create a dataset from existing semantic resources . |
| Outcome: | The proposed method improves the performance of existing lexical substitution systems on the existing benchmarks. |
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation (2020.coling-main)
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| Challenge: | Cross-lingual Machine Reading Comprehension (CLMRC) is a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi, and Vietnamese. |
| Approach: | They propose a novel approach to augment cross-lingual machine reading comprehension by combining knowledge from multiple language branch models into a single model for all target languages. |
| Outcome: | Extensive experiments on two CLMRC benchmarks show the proposed method is effective and robust to data noises. |
MRF-Chat: Improving Dialogue with Markov Random Fields (2021.emnlp-main)
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| Challenge: | Existing approaches to deep learning for open-domain dialogue include training end-to-end models to learn various conversational features like emotional content of response, symbolic transitions of dialogue contexts and persona of the agent and the user, among others. |
| Approach: | They propose a probabilistic approach using Markov Random Fields to augment existing deep-learning methods for improved next utterance prediction. |
| Outcome: | The proposed approach significantly improves the performance of existing state-of-the-art retrieval models for open-domain conversational agents. |
Self-training Improves Pre-training for Natural Language Understanding (2021.naacl-main)
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Jingfei Du, Edouard Grave, Beliz Gunel, Vishrav Chaudhary, Onur Celebi, Michael Auli, Veselin Stoyanov, Alexis Conneau
| Challenge: | Unsupervised pretraining has led to improvements in natural language understanding . a data augmentation method can be used to generate labels for unlabeled examples . |
| Approach: | They propose a semi-supervised method which uses unlabeled data to retrieve sentences from a database of billions of unlabed sentences crawled from the web. |
| Outcome: | The proposed method improves on standard text classification benchmarks by 2.6% and knowledge distillation by few shots. |