Papers with supervised task
CEAN: Contrastive Event Aggregation Network with LLM-based Augmentation for Event Extraction (2024.eacl-long)
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| Challenge: | Event Extraction is a crucial yet arduous task in natural language processing (NLP), as its performance is hindered by laborious data annotation. |
| Approach: | They propose a Contrastive Event Aggregation Network with LLM-based Augmentation to promote low-resource learning and reduce data noise for event extraction. |
| Outcome: | The proposed approach achieves new state-of-the-art results on the ACE2005 and ERE-EN datasets. |
You Only Need Attention to Traverse Trees (P19-1)
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| Challenge: | Recent research has focused on sentence representations. |
| Approach: | They propose a tree-based model that captures phrase-level syntax and word-level dependencies by doing recursive traversal with attention. |
| Outcome: | a new model captures phrase-level syntax and word-level dependencies with attention. |
Multilingual Word Segmentation: Training Many Language-Specific Tokenizers Smoothly Thanks to the Universal Dependencies Corpus (L18-1)
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| Challenge: | Towards language scalability, major progress has been achieved in multilingual language technology in recent years. |
| Approach: | They propose a tokenizer that can be trained from any Universal Dependencies corpus dataset . they argue that tokenization should be seen as a supervised task and scalability requires a software engineering process across languages. |
| Outcome: | The proposed tokenizer can be trained from any dataset in the corpus UD2 . the proposed software tool relies on elephant to perform the training . |
On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings (P19-1)
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| Challenge: | idiomatic phrases have a non-compositional meaning, meanings of which can be derived from constituents and their grammatical relations. |
| Approach: | They propose to combine hierarchical and distributional information to blend hierarchic and distribution-based hierarchies to detect compositionality for noun phrases. |
| Outcome: | The proposed technique achieves significant improvements over state-of-the-art models based on distributional information and a weighted average of the distributional similarity and p-like function. |
Misspelling Oblivious Word Embeddings (N19-1)
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Aleksandra Piktus, Necati Bora Edizel, Piotr Bojanowski, Edouard Grave, Rui Ferreira, Fabrizio Silvestri
| Challenge: | Existing word embeddings have limited applicability to malformed texts . misspellings are frequent and embeddable for words that have not been observed at training time . |
| Approach: | They propose a method to learn word embeddings that are resilient to misspellings . they use FastText with subwords to train embeddables on a new dataset . |
| Outcome: | The proposed method is tested on a publicly available dataset. |
Scalable and Domain-General Abstractive Proposition Segmentation (2024.findings-emnlp)
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| Challenge: | Several recent studies have demonstrated the utility of proposition segmentation for downstream tasks. |
| Approach: | They propose a scalable, yet accurate, proposition segmentation model that can be supervised by LLMs. |
| Outcome: | The proposed model improves on training on annotated datasets and shows that it is easy to use. |
InferLite: Simple Universal Sentence Representations from Natural Language Inference Data (D18-1)
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| Challenge: | InferLite is a lightweight version of InferSent that does not use recurrent layers and can generalize to multiple pre-trained word embeddings. |
| Approach: | They propose a lightweight version of InferSent that does not use recurrent layers and operates on a collection of pre-trained word embeddings. |
| Outcome: | The proposed model outperforms existing models that learn generic embeddings in an unsupervised setting, often requiring several days or weeks to train. |
Ambiguous Learning from Retrieval: Towards Zero-shot Semantic Parsing (2023.acl-long)
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Shan Wu, Chunlei Xin, Hongyu Lin, Xianpei Han, Cao Liu, Jiansong Chen, Fan Yang, Guanglu Wan, Le Sun
| Challenge: | Existing neural semantic parsers require a large amount of training data which is expensive and difficult to obtain. |
| Approach: | They propose a framework for a supervised retrieval system based on pretrained language models . they propose ambiguous supervision to improve the precision and coverage of the task . |
| Outcome: | The proposed approach outperforms state-of-the-art zero-shot parsing methods in ambiguous supervision. |