Papers with LCS
LEMUR: A Corpus for Robust Fine-Tuning of Multilingual Law Embedding Models for Retrieval (2026.eacl-srw)
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| Challenge: | Existing large language models are not designed for semantic retrieval and PDF-based legislative sources introduce substantial noise due to imperfect text extraction. |
| Approach: | They propose a large-scale multilingual corpus of EU environmental legislation constructed from 24,953 official EUR-Lex PDF documents covering 25 languages. |
| Outcome: | The proposed model improves Top-k retrieval accuracy in monolingual and bilingual settings . it also improves accuracy in low- and high-resource languages . |
Rethinking Negative Instances for Generative Named Entity Recognition (2024.findings-acl)
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| Challenge: | Named Entity Recognition (NER) models are constrained by a pre-defined label set and require extensive human annotations, which limits their flexibility and adaptability to unseen tasks. |
| Approach: | They propose a Generative NER system that shows improved zero-shot performance across unseen entity domains by introducing contextual information and delineating label boundaries. |
| Outcome: | The proposed model outperforms state-of-the-art methods in zero-shot evaluation. |
Active Learning for Rumor Identification on Social Media (2021.findings-emnlp)
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| Challenge: | Existing methods for rumor tracking depend on a significant amount of labeled data. |
| Approach: | They propose an Active-Transfer Learning strategy to identify rumors with limited amount of annotated data. |
| Outcome: | The proposed approach achieves faster convergence in terms of the F-score while requiring fewer annotated samples (42% of the whole dataset for the best model). |
CaLcs: Continuously Approximating Longest Common Subsequence for Sequence Level Optimization (D18-1)
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| Challenge: | Maximum-likelihood estimation (MLE) is widely used for text-generation based natural language processing applications. |
| Approach: | They propose a method to train models with maximum-likelihood estimation using a differentiable surrogate of longest common subsequence measure that captures sequence-level structure similarity. |
| Outcome: | Experimental results show that the proposed approach improves on the current MLE approach for downstream tasks like text summarization and machine translation. |
LCS: A Language Converter Strategy for Zero-Shot Neural Machine Translation (2024.findings-acl)
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| Challenge: | Existing LT strategies cannot indicate the desired target language on zero-shot translation, i.e., the off-target issue. |
| Approach: | They propose a language converter strategy that embeds the target language into the top encoder layers to mitigate confusion in the encoder and ensures stable language indication for the decoder. |
| Outcome: | The proposed language converter strategy significantly mitigates off-target issue on multiUN, TED, and OPUS-100 datasets. |
Contextualized Semantic Distance between Highly Overlapped Texts (2023.findings-acl)
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| Challenge: | Conventional semantic metrics are based on word representations and are vulnerable to disturbance of overlapped components with similar representations. |
| Approach: | They propose a mask-and-predict strategy to evaluate the semantic distance between the overlapped sentences using words in the longest common sequence as neighboring words and use masked language modeling to predict their positions. |
| Outcome: | The proposed method outperforms the state-of-the-art in domain adaption by a huge margin. |
GaLa: Hypergraph-Guided Visual Language Models for Procedural Planning (2026.findings-acl)
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| Challenge: | Existing methods for procedural planning over-rely on visual inputs and lack structured semantic information. |
| Approach: | They propose a vision–language framework for multimodal procedural planning that exploits implicit spatial relations and deep semantics encoded in object attributes. |
| Outcome: | The proposed framework outperforms existing methods in terms of execution success rate, LCS, and planning correctness. |