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Found 14 papers, 8 papers with code
Machine Reading, Fast and Slow: When Do Models “Understand” Language?
Two of the most fundamental issues in Natural Language Understanding (NLU) at present are: (a) how it can established whether deep learning-based models score highly on NLU benchmarks for the ”right” reasons; and (b) what those reasons would even be.
Can Edge Probing Tests Reveal Linguistic Knowledge in QA Models?
We find that EP test results do not change significantly when the fine-tuned model performs well or in adversarial situations where the model is forced to learn wrong correlations.
Explaining Interactions Between Text Spans
Reasoning over spans of tokens from different parts of the input is essential for natural language understanding (NLU) tasks such as fact-checking (FC), machine reading comprehension (MRC) or natural language inference (NLI).
Implications of Annotation Artifacts in Edge Probing Test Datasets
However, a large body of research claims that the tests necessarily do not measure the LLM's capacity to encode knowledge, but rather reflect the classifiers' ability to learn the problem.
Machine Reading, Fast and Slow: When Do Models "Understand" Language?
Two of the most fundamental challenges in Natural Language Understanding (NLU) at present are: (a) how to establish whether deep learning-based models score highly on NLU benchmarks for the 'right' reasons; and (b) to understand what those reasons would even be.
Can Edge Probing Tasks Reveal Linguistic Knowledge in QA Models?
We find that EP test results do not change significantly when the fine-tuned model performs well or in adversarial situations where the model is forced to learn wrong correlations.
Intent Features for Rich Natural Language Understanding
Complex natural language understanding modules in dialog systems have a richer understanding of user utterances, and thus are critical in providing a better user experience.
Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models
Recent research has demonstrated that large pre-trained language models reflect societal biases expressed in natural language.
Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers
Current state-of-the-art models for named entity recognition (NER) are neural models with a conditional random field (CRF) as the final layer.
Multiple Word Embeddings for Increased Diversity of Representation
Most state-of-the-art models in natural language processing (NLP) are neural models built on top of large, pre-trained, contextual language models that generate representations of words in context and are fine-tuned for the task at hand.
Computationally Efficient NER Taggers with Combined Embeddings and Constrained Decoding
The CRF layer is used to facilitate global coherence between labels, and the contextual embeddings provide a better representation of words in context.
Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLP
We introduce Baseline: a library for reproducible deep learning research and fast model development for NLP.
Text Extraction and Retrieval from Smartphone Screenshots: Building a Repository for Life in Media
Effective and efficient Information Extraction and Retrieval from digital screenshots is a crucial prerequisite to successful use of screen data.
Keyword and Keyphrase Extraction Using Centrality Measures on Collocation Networks
Keyword and keyphrase extraction is an important problem in natural language processing, with applications ranging from summarization to semantic search to document clustering.
