Papers by Sangwhan Moon
Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives (2020.findings-emnlp)
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| Challenge: | Modern dialog managers face the challenge of having to fulfill human-level conversational skills as part of common user expectations. |
| Approach: | They propose to extract the intent argument of non-canonical directives in a natural language format and build a parallel corpus for this purpose. |
| Outcome: | The proposed method extracts the intent argument of non-canonical directives in a natural language format, which may yield more accurate parsing. |
PatchBERT: Just-in-Time, Out-of-Vocabulary Patching (2020.emnlp-main)
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| Challenge: | a pre-trained language model with low OOV can improve performance for transfer learning . a vocabulary surrogate can provide performance boosts with no additional computation cost . |
| Approach: | They propose multiple methods to mitigate OOV during downstream task fine-tuning . they demonstrate that vocabulary surrogates can provide performance boosts with no additional computation cost . |
| Outcome: | The proposed methods improve performance with the same parameter count when combined with fine-tuning. |
StyleKQC: A Style-Variant Paraphrase Corpus for Korean Questions and Commands (2022.lrec-1)
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| Challenge: | Especially for questions and commands, style-variant paraphrasing can be crucial in tone and manner. |
| Approach: | They propose a corpus construction scheme that considers intent and formality of directives in Korean language. |
| Outcome: | The proposed method is validated by a corpus construction scheme on Korean topics. |
Learning How to Translate North Korean through South Korean (2022.lrec-1)
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| Challenge: | Existing NLP systems cannot properly handle North Korean inputs, despite limited data . Several NLP researchers have been working on the Korean language . |
| Approach: | They propose to manually create evaluation data for automatic alignment and machine translation, and investigate automatic alignment methods suitable for North Korean. |
| Outcome: | The proposed model trained by North Korean bilingual data significantly boosts translation accuracy compared to existing South Korean models in zero-shot settings. |
Parameter-Efficient Korean Character-Level Language Modeling (2023.eacl-main)
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| Challenge: | Character-level language modeling has been shown to perform well on highly agglutinative or morphologically rich languages while using only a small fraction of the parameters required by (sub)word models. |
| Approach: | They propose a “three-hot” embedding and decoding scheme that exploits the decomposability of Korean characters to model at the syllable level but using only jamo-level representations. |
| Outcome: | The proposed model reduces the embedding parameters by 99.6% and does not lose translation quality compared to the baseline model. |
Two Counterexamples to Tokenization and the Noiseless Channel (2024.lrec-main)
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| Challenge: | Nevertheless, Rényi efficiency is not perfect and the metric is difficult to evaluate because training multiple tokenizers can be prohibitively expensive and takes days or weeks. |
| Approach: | They propose to use Rényi efficiency as an intrinsic mechanism to evaluate a tokenizer for NLP tasks without the expensive step of training multiple models with different tokenizers. |
| Outcome: | The proposed metric is better correlated to downstream model performance than a percentile frequency metric. |
Jamo Pair Encoding: Subcharacter Representation-based Extreme Korean Vocabulary Compression for Efficient Subword Tokenization (2020.lrec-1)
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| Challenge: | a vocabulary size problem is unsolved in multilingual pre-training tasks. |
| Approach: | They propose two algorithms that increase the elasticity of budget for building the vocabulary in Byte-Pair Encoding inspired tokenizers, and reduce the cost of supporting Korean in a multilingual model. |
| Outcome: | The proposed method reduces the cost of supporting Korean in a multilingual model. |
OpenKorPOS: Democratizing Korean Tokenization with Voting-Based Open Corpus Annotation (2022.lrec-1)
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| Challenge: | Korean uses spaces at larger-than-word boundaries, unlike other East-Asian languages. |
| Approach: | They propose to use Korean morphological analyzers to provide a sequence of morpheme-level tokens, losing information in the tokenization process. |
| Outcome: | The proposed scheme improves existing tagging scheme and makes it friendlier to generative tasks. |