Semantic structure in deep learning
E Pavlick - Annual Review of Linguistics, 2022 - annualreviews.org
Deep learning has recently come to dominate computational linguistics, leading to claims of
human-level performance in a range of language processing tasks. Like much previous …
human-level performance in a range of language processing tasks. Like much previous …
Explainability for large language models: A survey
Large language models (LLMs) have demonstrated impressive capabilities in natural
language processing. However, their internal mechanisms are still unclear and this lack of …
language processing. However, their internal mechanisms are still unclear and this lack of …
Towards faithful model explanation in nlp: A survey
End-to-end neural Natural Language Processing (NLP) models are notoriously difficult to
understand. This has given rise to numerous efforts towards model explainability in recent …
understand. This has given rise to numerous efforts towards model explainability in recent …
From natural language processing to neural databases
In recent years, neural networks have shown impressive performance gains on long-
standing AI problems, such as answering queries from text and machine translation. These …
standing AI problems, such as answering queries from text and machine translation. These …
Specializing word embeddings (for parsing) by information bottleneck
Pre-trained word embeddings like ELMo and BERT contain rich syntactic and semantic
information, resulting in state-of-the-art performance on various tasks. We propose a very …
information, resulting in state-of-the-art performance on various tasks. We propose a very …
Voxelwise encoding models show that cerebellar language representations are highly conceptual
There is a growing body of research demonstrating that the cerebellum is involved in
language understanding. Early theories assumed that the cerebellum is involved in low …
language understanding. Early theories assumed that the cerebellum is involved in low …
Interpretation of black box nlp models: A survey
S Choudhary, N Chatterjee, SK Saha - arXiv preprint arXiv:2203.17081, 2022 - arxiv.org
An increasing number of machine learning models have been deployed in domains with
high stakes such as finance and healthcare. Despite their superior performances, many …
high stakes such as finance and healthcare. Despite their superior performances, many …
[HTML][HTML] Predicting semantic similarity between clinical sentence pairs using transformer models: Evaluation and representational analysis
M Ormerod, J Martínez del Rincón… - JMIR Medical …, 2021 - medinform.jmir.org
Background Semantic textual similarity (STS) is a natural language processing (NLP) task
that involves assigning a similarity score to 2 snippets of text based on their meaning. This …
that involves assigning a similarity score to 2 snippets of text based on their meaning. This …
[图书][B] Distributional semantics
A Lenci, M Sahlgren - 2023 - books.google.com
Distributional semantics develops theories and methods to represent the meaning of natural
language expressions, with vectors encoding their statistical distribution in linguistic …
language expressions, with vectors encoding their statistical distribution in linguistic …