Papers with semantic and syntactic tasks
CogCompNLP: Your Swiss Army Knife for NLP (L18-1)
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Daniel Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling, Dan Roth
| Challenge: | a corpus-reader module supports popular corpora, feature extraction and annotation modules for semantic and syntactic tasks. |
| Approach: | They propose a library that provides modules to address different challenges . they provide a corpus-reader module that supports popular corpora in the NLP community . |
| Outcome: | The proposed library simplifies the process of design and development of NLP applications by providing modules to address different challenges. |
Attention on Multiword Expressions: A Multilingual Study of BERT-based Models with Regard to Idiomaticity and Microsyntax (2025.findings-naacl)
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| Challenge: | Specifically, models fine-tuned on semantic tasks tend to distribute attention to idiomatic expressions more evenly across layers. |
| Approach: | They analyze attention patterns of encoder-only models towards two distinct types of Multiword Expressions (MWEs) idioms present challenges in semantic non-compositionality, while MSUs demonstrate unconventional syntactic behavior that does not conform to standard grammatical categorizations. |
| Outcome: | The proposed models show that fine-tuned models allocate attention to idiomatic expressions more evenly across layers. |
Word Embeddings for Code-Mixed Language Processing (D18-1)
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| Challenge: | Existing bilingual word embedding techniques are not ideal for code-mixed text processing and there is a need for learning multilingual word embeds from code-mixed texts. |
| Approach: | They propose to use bilingual word embedding techniques to train skip-grams on synthetic code-mixed text generated through linguistic models of code- mixing to perform two tasks. |
| Outcome: | The proposed embedding technique performs better on semantic and syntactic tasks than the existing embeddable techniques on sentiment analysis and POS tagging tasks. |
Multilingual Pretraining for Pixel Language Models (2025.emnlp-main)
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| Challenge: | PIXEL-M4 model pretrains on four visually and linguistically diverse languages . previous work on pixel-based language models focused on monolingual pretraining on English data . |
| Approach: | They propose a pixel-based language model that is pretrained on four visually diverse languages. |
| Outcome: | The proposed model outperforms an English-only counterpart on non-Latin scripts on semantic and syntactic tasks. |