Papers with neural embeddings
SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features (2022.aacl-main)
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| Challenge: | Abstract Meaning Representation (S3BERT) embeddings are composed of explainable sub-embeddings that emphasize various sentence meaning features. |
| Approach: | They propose to induce Semantically Structured Sentence BERT embeddings (S3BERT) that emphasize various sentence meaning features. |
| Outcome: | The proposed model shows high correlation to human similarity ratings, but lacks interpretability. |
A Corpus to Learn Refer-to-as Relations for Nominals (L18-1)
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| Challenge: | Existing work on how to learn refer-to-as relations from large unlabeled corpora lacks coreferential information. |
| Approach: | They propose to use Wikipedia to generate coreferential neural embeddings for nominals . they use coreference resolution as a proxy to evaluate the neural embeds for noun phrases . |
| Outcome: | The proposed dataset can be leveraged to construct representations for coreferential nominals from Wikipedia. |
Lexical Relation Mining in Neural Word Embeddings (2020.coling-main)
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| Challenge: | Conventionally, lexical relations in word vector space have been defined by collections of relatively consistent relationships, or vector offsets, between word-pairs. |
| Approach: | They propose to use Word2Vec space of word-pairs to find lexical relations . they also demonstrate a method for approximating the presence of syntactic and semantic relations based on word vectors extracted from word embeddings. |
| Outcome: | The proposed method outperforms other validated methods in the presence of noisy offsets. |
Social Biases in NLP Models as Barriers for Persons with Disabilities (2020.acl-main)
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| Challenge: | toxicity prediction and sentiment analysis models perpetuate undesirable social biases from the data on which they are trained. |
| Approach: | They propose to use toxicity prediction and sentiment analysis to examine whether NLP models perpetuate undesirable biases towards mentions of disability. |
| Outcome: | The proposed models contain undesirable biases towards mentions of disability in two English language models. |
Sequential Modelling of the Evolution of Word Representations for Semantic Change Detection (2020.emnlp-main)
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| Challenge: | Existing models that detect semantically shifted words do not account for its evolution through time. |
| Approach: | They propose three variants of sequential models for detecting semantically shifted words . they demonstrate that temporal modelling of word representations yields a clear-cut advantage . |
| Outcome: | The proposed models account for the changes in word representations over time. |
Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit? (2020.lrec-1)
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| Challenge: | In recent years, vectors derived from neural network training have replaced count-based distributional semantic models as a de facto standard for word representation in NLP. |
| Approach: | They propose to evaluate count models and word embeddings on thematic fit estimation by taking into account a larger number of parameters and verb roles and introducing dependency-based embedders in the comparison. |
| Outcome: | The proposed model outperforms count models and word embeddings in thematic fit estimation tasks while introducing dependency-based embedders. |
LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding (2026.acl-long)
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Zhivar Sourati, Zheng Wang, Marianne Menglin Liu, Yazhe Hu, Mengqing Guo, Sujeeth Bharadwaj, Kyu J. Han, Tao Sheng, Sujith Ravi, Morteza Dehghani, Dan Roth
| Challenge: | Conventional retrieval-augmented generation (RAG) methods encode content in isolated chunks during ingestion, losing structural and cross-page dependencies, and retrieve a fixed number of pages at inference. |
| Approach: | They propose a Layout-Aware Dynamic RAG framework that encodes content in isolated chunks during ingestion and retrieves a fixed number of pages at inference. |
| Outcome: | Experiments on MMLongBench-Doc, LongDocURL, DUDE, and MP-DoxVQA show that LAD-RAG improves retrieval, achieving over 90% perfect recall on average without any top-k tuning, and outperforming baseline retrievers by up to 20% in recall at comparable noise levels. |