Papers with non-neural models
Back to Patterns: Efficient Japanese Morphological Analysis with Feature-Sequence Trie (2023.acl-short)
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| Challenge: | Accurate neural models are less efficient than non-neural models and are useless for processing billions of social media posts and handling user queries. |
| Approach: | They propose to make fast pattern-based NLP methods as accurate as possible . they propose a morphological analyzer for Japanese that induces reliable patterns . |
| Outcome: | The proposed method induces reliable patterns from a morphological dictionary and annotated data in Japanese. |
Neural Unsupervised Parsing Beyond English (D19-61)
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| Challenge: | Unsupervised parsing is a task that can be learned without substantial prior knowledge. |
| Approach: | They train an unsupervised model for Arabic, Chinese, English, and German to learn syntactic structure from unlabeled text. |
| Outcome: | The PRPN architecture outperforms trivial baselines and acquires at least some parsing ability for all languages. |
BART-TL: Weakly-Supervised Topic Label Generation (2021.eacl-main)
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| Challenge: | Existing methods for topic labeling use weak labelers to train rankers . recent studies show that weakly-supervised methods can produce meaningful labels . |
| Approach: | They propose a weakly-supervised method for assigning topic labels to models by using weak labelers. |
| Outcome: | The proposed model can generate valuable and novel labels in a weakly-supervised manner and can be improved by adding other weak labelers or distant supervision on similar tasks. |
Non-neural Models Matter: a Re-evaluation of Neural Referring Expression Generation Systems (2022.acl-long)
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| Challenge: | In recent years, neural models have outperformed rule-based and classic approaches in NLG. |
| Approach: | They evaluate two English datasets and evaluate their performance using automatic and human evaluations. |
| Outcome: | The proposed model outperforms rule-based and classic approaches on two English datasets and is compared with human-based models. |