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of 24
pro vyhledávání: '"Choudhury, Sagnik Ray"'
Autor:
Choudhury, Sagnik Ray, Kalra, Jushaan
Edge probing tests are classification tasks that test for grammatical knowledge encoded in token representations coming from contextual encoders such as large language models (LLMs). Many LLM encoders have shown high performance in EP tests, leading
Externí odkaz:
http://arxiv.org/abs/2310.13856
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). However, existing highli
Externí odkaz:
http://arxiv.org/abs/2310.13506
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 wo
Externí odkaz:
http://arxiv.org/abs/2209.07430
There have been many efforts to try to understand what grammatical knowledge (e.g., ability to understand the part of speech of a token) is encoded in large pre-trained language models (LM). This is done through `Edge Probing' (EP) tests: supervised
Externí odkaz:
http://arxiv.org/abs/2109.07102
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. However, these models are often created from scratch, for specific clients a
Externí odkaz:
http://arxiv.org/abs/2104.08701
Autor:
Stańczak, Karolina, Choudhury, Sagnik Ray, Pimentel, Tiago, Cotterell, Ryan, Augenstein, Isabelle
Recent research has demonstrated that large pre-trained language models reflect societal biases expressed in natural language. The present paper introduces a simple method for probing language models to conduct a multilingual study of gender bias tow
Externí odkaz:
http://arxiv.org/abs/2104.07505
Current state-of-the-art models for named entity recognition (NER) are neural models with a conditional random field (CRF) as the final layer. Entities are represented as per-token labels with a special structure in order to decode them into spans. C
Externí odkaz:
http://arxiv.org/abs/2010.04362
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. The improvem
Externí odkaz:
http://arxiv.org/abs/2009.14394
Current State-of-the-Art models in Named Entity Recognition (NER) are neural models with a Conditional Random Field (CRF) as the final network layer, and pre-trained "contextual embeddings". The CRF layer is used to facilitate global coherence betwee
Externí odkaz:
http://arxiv.org/abs/2001.01167
Autor:
Chiatti, Agnese, Cho, Mu Jung, Gagneja, Anupriya, Yang, Xiao, Brinberg, Miriam, Roehrick, Katie, Choudhury, Sagnik Ray, Ram, Nilam, Reeves, Byron, Giles, C. Lee
Daily engagement in life experiences is increasingly interwoven with mobile device use. Screen capture at the scale of seconds is being used in behavioral studies and to implement "just-in-time" health interventions. The increasing psychological brea
Externí odkaz:
http://arxiv.org/abs/1801.01316