Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection (2022.coling-1)
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| Challenge: | Experimental results show that cross-language data expansion results in performance degradation. |
| Approach: | They leverage cross-language data expansion and retraining to enhance neural Event Detection on English ACE corpus. |
| Outcome: | The proposed method improves ED performance by 1.6% over the straight data combination. |
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| Challenge: | Event detection is a key part of event extraction, but there are two issues with word-based models in languages without natural delimiters, such as Chinese. |
| Approach: | They propose a framework that can solve the problem of word- trigger mismatch . they also use an external knowledge base to model polysemous characters and words . |
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Retrieving Relevant Context to Align Representations for Cross-lingual Event Detection (2023.findings-acl)
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| Challenge: | Existing approaches to cross-lingual transfer learning for event detection are mixed with event-discriminative context. |
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Unleash GPT-2 Power for Event Detection (2021.acl-long)
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| Challenge: | Event Detection (ED) aims to recognize mentions of events and their types in text. |
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Neural Cross-Lingual Event Detection with Minimal Parallel Resources (D19-1)
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| Challenge: | Existing methods for event detection (ED) rely on high-performance machine translation systems or manually aligned documents to achieve a decent performance. |
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Tagged Back-translation Revisited: Why Does It Really Work? (2020.acl-main)
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| Challenge: | In this paper, we show that neural machine translation systems trained on large back-translated data overfit some of the characteristics of machine-transcribed texts. |
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Don’t Augment, Rewrite? Assessing Abusive Language Detection with Synthetic Data (2024.findings-acl)
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| Challenge: | Existing datasets for abusive language detection and content moderation are limited by regulatory bodies and social media platforms. |
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MINION: a Large-Scale and Diverse Dataset for Multilingual Event Detection (2022.naacl-main)
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| Challenge: | Existing methods for ED in IE and NLP focus on feature-based models to feature-driven models. |
| Approach: | They propose to use a multilingual dataset to annotate events for 8 different languages . they demonstrate the challenges and transferability of ED across languages in MINION . |
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A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM𝛥 Integration into Upcycled MoE (2026.acl-long)
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Hao Zhou, Tianhao Li, Zhijun Wang, Shuaijie She, Linjuan Wu, Hao-Ran Wei, Baosong Yang, Jiajun Chen, Shujian Huang
| Challenge: | Large Language Models (LLMs) are expensive and require extensive Continued Pre-Training and data-intensive alignment to expand. |
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Counterfactual Data Augmentation for Neural Machine Translation (2021.naacl-main)
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| Challenge: | Neural machine translation models often rely on large-scale parallel corpora for training, exhibiting degraded performance on low-resource languages. |
| Approach: | They propose a method that interprets language models and phrasal alignment causally and generates augmented parallel translation corpora by sampling new source phrases from a masked language model. |
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A Discriminative Neural Model for Cross-Lingual Word Alignment (D19-1)
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| Challenge: | a novel word alignment model for machine translation has been developed for a number of languages . explicit word-to-word alignments have largely been lost in neural MT systems . |
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