Papers with dictionary-based methods
Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification (2024.acl-long)
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| Challenge: | Existing annotation codebook is labor-intensive for coding events from large datasets. |
| Approach: | They propose to use existing annotation codebook to classify political relations without extensive annotations. |
| Outcome: | The proposed methods outperform dictionary-based methods and the existing ontology annotation codebook and improve interpretability and efficiency. |
SLoW: Select Low-frequency Words! Automatic Dictionary Selection for Translation on Large Language Models (2025.emnlp-main)
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| Challenge: | Existing large language models only support hundreds of languages, and they are usually limited in English. |
| Approach: | They propose a task to automatically select which dictionary to use to enhance translation . they call it Select Low-frequency Words!, which inherits advantage of dictionary-based methods . |
| Outcome: | The proposed method can save tokens and improve translation performance on 100 languages. |
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents (2023.findings-acl)
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Mehrad Moradshahi, Tianhao Shen, Kalika Bali, Monojit Choudhury, Gael de Chalendar, Anmol Goel, Sungkyun Kim, Prashant Kodali, Ponnurangam Kumaraguru, Nasredine Semmar, Sina Semnani, Jiwon Seo, Vivek Seshadri, Manish Shrivastava, Michael Sun, Aditya Yadavalli, Chaobin You, Deyi Xiong, Monica Lam
| Challenge: | X-RiSAWOZ dataset has more than 18,000 human-verified dialogue utterances for each language . Xiaoping and Xinhui are the main challenges for task-oriented dialogue research . |
| Approach: | They develop a toolkit to accelerate the post-editing of a new language dataset after translation . their dataset, code, and toolkit are released open-source . |
| Outcome: | The proposed toolkit accelerates the post-editing of a new language dataset after translation. |
Auto-Encoding Dictionary Definitions into Consistent Word Embeddings (D18-1)
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| Challenge: | Monolingual dictionaries are widespread and semantically rich resources. |
| Approach: | They propose a model that learns to compute word embeddings by processing dictionary definitions and trying to reconstruct them. |
| Outcome: | The proposed model shows strong performance when trained exclusively on dictionary data and generalizes in one shot. |